Function homology screening

ABSTRACT

A method of screening biologically active agent based on the analysis of complex biological responses in culture. Methods for selecting cells and culture conditions for such screens are provided, as well as the identification of an optimized set of discrete parameters to be measured, and the use of biomap analysis for rapid identification and characterization of drug candidates, genetic sequences acting pathways, and the like. A feature of the invention is simultaneous screening of a large number of cellular pathways, and the rapid identification of compounds that cause cellular responses.

FIELD OF THE INVENTION

The field of the invention is the discrimination between differentcellular pathways and their use in the determination of the effect ofagents on cell cultures.

BACKGROUND OF THE INVENTION

Pharmaceutical drug discovery, a multi-billion dollar industry, involvesthe identification and validation of therapeutic targets, as well as theidentification and optimization of lead compounds. The explosion innumbers of potential new targets and chemical entities resulting fromgenomics and combinatorial chemistry approaches over the past few yearshas placed enormous pressure on screening programs. The rewards foridentification of a useful drug are enormous, but the percentages ofhits from any screening program are generally very low. Desirablecompound screening methods solve this problem by both allowing for ahigh throughput so that many individual compounds can be tested; and byproviding biologically relevant information so that there is a goodcorrelation between the information generated by the screening assay andthe pharmaceutical effectiveness of the compound.

Some of the more important features for pharmaceutical effectiveness arespecificity for the targeted cell or disease, a lack of toxicity atrelevant dosages, and specific activity of the compound against itsmolecular target. Therefore, one would like to have a method forscreening compounds or libraries of compounds that allows simultaneousevaluation for the effect of a compound on different cellular pathways,where the assay predicts aspects of clinical relevance and potentiallyof future in vivo performance.

While collecting information about multiple aspects of pharmacologicactivity is useful because it provides a more complete analysis of thecompound, it also makes the data analysis more difficult, becausemultiple parameters must be considered. Developments in computingtechnologies can provide solutions, but must be tied into the matrix ofbiological information.

In addition, cellular physiology involves multiple pathways, wherepathways split and join, redundancies in performing specific actions andresponding to a change in one pathway by modifying the activity of adifferent pathway. In order to understand how a candidate drug is actingand whether it will have the desired effect, it is necessary to know,not only the target protein with which the drug reacts, but whether theinhibition of the protein activity will result in the desired response.The development of screening assays that can provide better, faster andmore efficient prediction of mechanisms of action, cellular effects andclinical drug performance is of great interest in a number of fields,and is addressed in the present invention. It is an object of theinvention to provide a method for screening for inhibitors or modulatorsof cellular processes, which provide multiparameter information aboutthe action of the agents tested on multiple cellular pathways.

Relevant Literature

In many assays, cell-free components such as enzymes and theirsubstrates are used for compound screening. For example, U.S. Pat. No.4,568,649 describes ligand detection systems which employ scintillationcounting. In these methods, the therapeutic utility of compoundsidentified in such assays is presumed from a large body of otherevidence previously identifying that a particular enzyme or target maybe important to a disease process.

Cell based assays include a variety of methods to measure metabolicactivities of cells including: uptake of tagged molecules or metabolicprecursors, receptor binding methods, incorporation of tritiatedthymidine as a measure of cellular proliferation, uptake of protein orlipid biosynthesis precursors, the binding of radiolabeled or otherwiselabeled ligands; assays to measure calcium flux, and a variety oftechniques to measure the expression of specific genes or their geneproducts.

Compounds have also been screened for their ability to inhibit theexpression of specific genes in gene reporter assays. For example, Ashbyet al. U.S. Pat. No. 5,569,588; Rine and Ashby U.S. Pat. No. 5,777,888describe a genome reporter matrix approach for comparing the effect ofdrugs on a panel of reporter genes to reveal effects of a compound onthe transcription of a spectrum of genes in the genome.

Methods utilizing genetic sequence microarrays allow the detection ofchanges in expression patterns in response to stimulus. A few examplesinclude U.S. Pat. No. 6,013,437; Luria et al., “Method for identifyingtranslationally regulated genes”; U.S. Pat. No. 6,004,755, Wang,“Quantitative microarray hybridization assays”; and U.S. Pat. No.5,994,076, Chenchik et al., “Methods of assaying differentialexpression”. U.S. Pat. No. 6,146,830, Friend et al. “Method fordetermining the presence of a number of primary targets of a drug”.

Proteomics techniques have potential for application to pharmaceuticaldrug screening. These methods require technically complex analysis andcomparison of high resolution two-dimensional gels or other separationmethods, often followed by mass spectrometry (for reviews seeHatzimanikatis et al. (1999) Biotechnol Proq 15(3):312-8; Blackstock etal. (1999) Trends Biotechnol 17(3):121-7. A discussion of the uses ofproteomics in drug discovery may be found in Mullner et al. (1998)Arzneimittelforschung 48(1):93-5.

Various methods have been used to determine the function of a geneticsequence. The initial effort is often performed from sequenceinformation alone. Such techniques can reasonably determine if a newgene encodes a soluble or membrane-bound protein, a member of a knowngene family such as the immunoglobulin gene family or the tetraspan genefamily, or contains domains associated with particular functions (e.g.calcium binding, SH2 domains etc.). Multiple alignments against adatabase of known sequences are frequently calculated using an heuristicapproach, as described in Altschul et al. (1994) Nat. Genet. 6:119.

Alternatively, “reverse genetics” is used to identify gene function.Techniques include the use of genetically modified cells and animals. Atargeted gene may be “knocked out” by site specific recombination,introduction of anti-sense constructs or constructs encoding dominantnegative mutations, and the like (see, for some examples, U.S. Pat. No.5,631,153, Capecchi et al. for methods of creating transgenic animals;Lagna et al. (1998) Curr Top Dev Biol 36:75-98 for an overview of theuse of dominant negative constructs; and Nellen et al. (1993) TrendsBiochem Sci 18(11):419-23 for a review of anti-sense constructs).

Cells and animals may also be modified by the introduction of geneticfunction, through the introduction of functional coding sequencescorresponding to the genetic sequence of interest. General techniquesfor the creation of transgenic animals may be found in Mouse Geneticsand Transgenics: A Practical Approach (Practical Approach Series) by IanJ. Jackson (Editor), Catherine M. Abbott (Editor). While they haveproven useful in many ways, however, transgenic animals frequentlysuffer from problems of time and expense, as well as compensatorymechanisms, redundancies, pleiotropic genetic effects, and the lethalityof certain mutations.

Another approach for discovering the function of genes utilizes genechips or microarrays. DNA sequences representing all the genes in anorganism can be placed on miniature solid supports and used ashybridization substrates to quantitate the expression of all the genesrepresented in a complex mRNA sample, and assess the effect of aperturbation on gene expression. Methods utilizing genetic sequencemicroarrays can be applied to pharmaceutical target validation. In thesemethods, genetic modifications are evaluated for their effects on theexpression of particular genes. A few examples include U.S. Pat. No.6,013,437; Luria et al., “Method for identifying translationallyregulated genes”; U.S. Pat. No. 6,004,755, Wang, “Quantitativemicroarray hybridization assays”; and U.S. Pat. No. 5,994,076, Chenchiket al., “Methods of assaying differential expression”.

Gene reporter assays can also be used to characterize the effect ofgenetic modifications by their ability to inhibit the expression ofspecific genes in gene reporter assays. For example, Ashby et al. U.S.Pat. No. 5,569,588; Rine and Ashby U.S. Pat. No. 5,777,888 describe agenome reporter matrix approach for comparing the effect of drugs on apanel of reporter genes to reveal effects of a compound on thetranscription of a spectrum of genes in the genome.

SUMMARY OF THE INVENTION

Methods and compositions are provided for function homology screening bydiscriminating between different cellular pathways, both as to theeffect of genotype modification on cellular pathways and changes inparameters resulting from changes in the pathways, and using thediscrimination for determining the effect of an agent on a mammaliancell culture system simulating cellular functions, as in a cellularstate of interest, usually associated with a diseased state of amammalian host. Cells capable of responding to factors and simulatingthe state of interest are employed, where the factors enhance theresponse of the measured components of the phenotype to approximate theresponse in vivo to external agents. A sufficient number of factors areemployed to involve a plurality of pathways and a sufficient number ofparameters are selected to be involved with a plurality of pathways andprovide a robust response to the effect of a change in the environmentof the cells. A flexible, multiplex screening assay is provided forscreening and biological activity classification of biologically activeagents. Assays are performed in the presence of an agent of interest,whereby a level of at least about 3 markers is obtained associated withthe presence of the agent and the results compared to the level of themarkers observed in the absence of the agent. By employing reagents thatare known to have an effect on a pathway in conjunction with the agent,the pathway affected by the agent can be determined. The data resultingfrom the assays can be processed to provide robust comparisons betweendifferent environments and agents. Databases are provided so that agentsand their effects may be compared. Particularly, biomaps are providedallowing for ready comparison, —visual, mathematical and electronic—ofthe results of different assays involving the same or different agentswith assays involving the same or different reagents.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1. Assay combinations for screening inflammatory modulators. A.Expression of selected readout parameters on selected assay combinationsof HUVEC treated with proinflammatory cytokines. Confluent cultures ofHUVEC cells were treated with TNF-α (5 ng/ml), IFN-γ (100 ng/ml) or IL-1(1 ng/ml). After 24 hours, cultures were washed and evaluated for thepresence of the parameters ICAM-1 (1), VCAM-1 (2), E-selectin (3), IL-8(4), CD31 (5), HLA-DR (6) and MIG (7) by cell-based ELISA. For this,plates were inverted until dry, blocked with 1% Blotto for 1 hr, andtreated with primary antibodies (obtained from Pharmingen and BectonDickinson) at 1 ng/ml for 1 hr. Plates were washed and secondaryperoxidase-conjugated anti-mouse IgG antibody (Promega) at 1:2500 wasapplied for 1 hr. After washing, TMB substrate (Kierkegaard & Perry) wasadded and color developed. Development was stopped by addition of H₂SO₄and the absorbance at 450 nm (subtracting the background absorbance at650 nm) with a Molecular Devices plate reader. The relative expressionlevels of each parameter are indicated by the OD at 450 nm shown alongthe y-axis. The mean +/−SD from triplicate samples is shown. B.Expression of selected readout parameters on selected assay combinationsof HUVEC treated with cytokine combinations. Confluent cultures of HUVECcells were treated with TNF-α (5 ng/ml), IFN-γ (100 ng/ml) or TNF-α andIFN-γ. After 24 hours, cultures were washed and evaluated for thepresence of the parameters ICAM-1 (1), VCAM-1 (2), E-selectin (3), IL-8(4), CD31 (5), HLA-DR (6) and MIG (7) by cell-based ELISA performed asdescribed above. The relative expression levels of each parameter areindicated by the OD at 450 nm. The mean +/−SD from triplicate samplesare shown. C. Expression of selected readout parameters on selectedassay combinations of HUVEC treated with cytokine combinations.Confluent cultures of HUVEC cells were treated with TNF-α (5ng/ml)+IFN-γ (100 ng/ml) or TNF-α (5 ng/ml)+IFN-γ (100 ng/ml)+IL-1 (1ng/ml). After 24 hours, cultures were washed and evaluated for thepresence of the parameters ICAM-1 (1), VCAM-1 (2), E-selectin (3), IL-8(4), CD31 (5), HLA-DR (6) and MIG (7) by cell-based ELISA performed asdescribed above. The relative expression levels of each parameter areindicated by the OD at 450 nm. The mean +/−SD from triplicate samplesare shown. * indicates p<0.05 comparing results obtained with the twoseparate conditions, n=3.

FIG. 2. Assay combinations for screening inflammatory modulators.Confluent cultures of HU VEC cells were treated with combinations ofTNF-α (5 ng/ml), IFN-γ (200 ng/ml) and IL-1 (1 ng/ml) or base media.After 24 hours, cultures were washed and evaluated for the presence ofICAM-1 (1), VCAM-1 (2), E-selectin (3), IL-8 (4), CD31 (5), HLA-DR (6)and MIG (7) by cell-based ELISA performed as described in FIG. 1. A. Therelative expression level of each parameter is shown along the y-axis asaverage value of the OD measured at 450 nm of triplicate samples. B. Acolor-coded representation of the data shown in A. For each parameterand assay combination, the square is colored light gray if the parametermeasurement is unchanged (<20% above or below the measurement in thefirst assay combination (IL-1+TNF+IFN-γ) or p>0.05, n=3); white/grayhatched indicates that the parameter measurement is moderately increased(>20% but <50%); white indicates the parameter measurement is stronglyincreased (>50%); black/gray hatched indicates that the parametermeasurement is moderated decreased (>20% but <50%); black indicates thatthe parameter measurement is strongly decreased (>50% less than thelevel measured in the first assay combination). C. A tree diagramrepresentation of the biomaps prepared from data shown in A and B.Resulting biomaps were compared and analyzed by hierarchical clustering.Biomap relationships are visualized by a tree diagram in which a) eachterminal branch point represents the biomap prepared from the indicatedassay combination; b) the length of the vertical distance from the upperhorizontal line (no change and control patterns) to the termini arerelated to the extent of difference in the readout pattern from thereference pattern (IL-1+TNF-α+IFN-γ); and c) the distance along thebranches from one terminal pattern value to another reflects the extentof difference between them.

FIG. 3. Effect of neutralizing anti-TNF-α antibody on the expression ofreadout parameters in the inflammatory assay combination containingthree factors (IL-1+TNF-α+IFN-γ). Confluent cultures of HUVEC cells weretreated with TNF-α (5 ng/ml)+IFN-γ (200 ng/ml)+IL-1 (1 ng/ml) in thepresence or absence of neutralizing anti-TNF-α (R&D Systems) or controlGoat anti-IgG. After 24 hours, cultures were washed and evaluated forthe cell surface expression of ICAM-1 (1), VCAM-1 (2), E-selectin (3),IL-8 (4), CD31 (5), HLA-DR (6) and MIG (7) by cell-based ELISA performedas described in FIG. 1. A. The relative expression of each parameter isshown along the y-axis as average value of the OD measured at 450 nm oftriplicate samples. The mean +/−SD from triplicate samples are shown. *indicates p<0.05 comparing results obtained with anti-TNF-α to thecontrol. B. A color-coded representation of biomaps prepared from thedata shown in A. For each parameter and assay combination, the square iscolored light gray if the parameter measurement is unchanged (<20% aboveor below the measurement in the first assay combination(IL-1+TNF-α+IFN-γ) or p>0.05, n=3); white/gray hatched indicates thatthe parameter measurement is moderately increased (>20% but <50%); whiteindicates the parameter measurement is strongly increased (>50%);black/gray hatched indicates that the parameter measurement is moderateddecreased (>20% but <50%); black indicates that the parametermeasurement is strongly decreased (>50% less than the level measured inthe first assay combination).

FIG. 4. A and B. Effect of NFκB inhibitors nordihydroguaiaretic acid(NHGA) and pyrrolidine dithiocarbamate (PDTC), MAP kinase inhibitorPD098059, or ibuprofen on the expression of readout parameters in theinflammatory assay combination containing three factors(IL-1+TNF-α+IFN-γ). Confluent cultures of HUVEC cells were treated withTNF-α (5 ng/ml)+IFN-γ (200 ng/ml)+IL-1 (20 ng/ml) in the presence orabsence of (A) 10 μM NHGA, 200 μM PDTC or 9 μM PD098059; (B) 125-500 μMibuprofen. Compounds were tested at the highest concentration at whichthey were soluble, and/or did not result in loss of cells from theplate. After 24 hours, cultures were washed and evaluated for the cellsurface expression of ICAM-1 (1), VCAM-1 (2), E-selectin (3), IL-8 (4),CD31 (5), HLA-DR (6) and MIG (7) by cell-based ELISA performed asdescribed in FIG. 1. A color-coded representation of the biomapsprepared from the data is shown. For each parameter and assaycombination, the square is colored light gray if the parametermeasurement is unchanged (<20% above or below the measurement in thefirst assay combination (IL-1+TNF-α+IFN-γ), or p>0.05, n=3); white/grayhatched indicates that the parameter measurement is moderately increased(>20% but <50%); white indicates the parameter measurement is stronglyincreased (>50%); black/gray hatched indicates that the parametermeasurement is moderated decreased (>20% but <50%); black indicates thatthe parameter measurement is strongly decreased (>50% less than thelevel measured in the first assay combination).

FIG. 4C. Effect of compounds on the reference readout pattern in theinflammatory assay combination containing three factors(IL-1+TNF-α+IFN-γ). Confluent cultures of HUVEC cells were treated withTNF-α (5 ng/ml)+IFN-γ (200 ng/ml)+IL-1 (1 ng/ml) in the presence orabsence of compounds or agents as listed in Table 1. After 24 hours,cultures were washed and evaluated for the cell surface expression ofparameters of ICAM-1, VCAM-1, E-selectin, IL-8, CD31, HLA-DR and MIG bycell-based ELISA performed as described in FIG. 1. The resulting biomapswere compared (Table 1) and analyzed by hierarchical clustering. Biomaprelationships are visualized by a tree diagram in which a) each terminalbranch point represents the biomap prepared from the indicated assaycombination; b) the length of the vertical distance from the upperhorizontal line (no change and control patterns) to the termini arerelated to the extent of difference in the readout pattern from thereference pattern (IL-1+TNF-α+IFN-γ); and c) the distance along thebranches from one terminal pattern value to another reflects the extentof difference between them. Similar patterns are thus clusteredtogether. The figure illustrates the reproducibility of patternsresulting from treatment with a single drug in multiple experiments, andthose resulting from multiple drugs that target the same signalingpathway.

FIG. 5. Effect of neutralizing anti-TNF-α antibody or NFκB inhibitorsAA861 and nordihydroguaiaretic acid (NHGA) on readout patterns inmultiple assay combinations. Confluent cultures of HUVEC cells weretreated with TNF-α (5 ng/ml), IFN-γ (200 ng/ml), IL-1 (1 ng/ml), thecombination of TNF-α+IFN-γ+IL-1, or media in the presence or absence of5 μg/ml neutralizing anti-TNF-α (R&D Systems), 20 μM AA861 or 10 μMNHGA. After 24 hours, cultures were washed and evaluated for the cellsurface expression of ICAM-1 (1), VCAM-1 (2), E-selectin (3), IL-8 (4),and MIG (5) by cell-based ELISA performed as described in FIG. 1. Acolor-coded representation of the biomaps prepared from the data isshown, coded as described in FIG. 2B.

FIG. 6. Effect of a neutralizing anti-TNF-α antibody on readout patternsin multiple assay combinations. Confluent cultures of HUVEC cells weretreated with TNF-α (5 ng/ml), IL-1 (1 ng/ml), an activating antibodyagainst the TNF-α-receptor p55, (Act-anti-p55, 3 μg/ml, R&D Systems), ormedia in the presence or absence of neutralizing TNF-α antibody(Anti-TNF-α, 5 μg/ml, R&D Systems). After 24 hours, cultures were washedand evaluated for the cell surface expression of ICAM-1 (1), VCAM-1 (2),E-selectin (3), CD31 (4), and MIG (5) by cell-based ELISA performed asdescribed in FIG. 1. A color-coded representation of the biomapsprepared from the data is shown, coded as described in FIG. 2B.

FIG. 7. Effect of soluble TNF-α-receptor p55-Fc fusion protein (p55-Fc)on the expression of readout parameters in multiple assay combinations.A. Confluent cultures of HUVEC cells were treated with TNF-α (5ng/ml)+IFN-γ (100 ng/ml)+IL-1 (1 ng/ml) in the presence or absence ofp55-Fc (50 ng/ml, Pharmingen). After 24 hours, cultures were washed andevaluated for the cell surface expression of ICAM-1 (1), VCAM-1 (2),E-selectin (3), IL-8 (4), CD31 (5), HLA-DR (6) and MIG (7) by cell-basedELISA performed as described in FIG. 1. The relative expression of eachparameter is shown along the y-axis as average value of the OD measuredat 450 nm of triplicate samples. The mean +/−SD from triplicate samplesare shown. * indicates p<0.05 comparing results obtained with anti-TNF-αto the control. B. Confluent cultures of HUVEC cells were treated withTNF-α (5 ng/ml), IFN-γ (100 ng/ml), IL-1 (1 ng/ml), the combination ofTNF-α+IFN-γ+IL-1, or media in the presence or absence of p55-Fc (50ng/ml, Pharmingen). After 24 hours, cultures were washed and evaluatedfor the cell surface expression of ICAM-1 (1), VCAM-1 (2), E-selectin(3), IL-8 (4), and MIG (5) by cell-based ELISA performed as described inFIG. 1. A color-coded representation of the biomaps prepared from thedata is shown, coded as described in FIG. 2B. C. Confluent cultures ofHUVEC cells were treated with TNF-α (5 ng/ml), IL-1 (1 ng/ml), anactivating antibody against the TNF-α-receptor p55 (5 μg/ml,Pharmingen), or media with or without p55-Fc (50 ng/ml). After 24 hours,cultures were washed and evaluated for the cell surface expression ofICAM-1 (1), VCAM-1 (2), E-selectin (3), CD31 (4), and MIG (5) bycell-based ELISA performed as described in FIG. 1. A color-codedrepresentation of the biomaps prepared from the data is shown, coded asdescribed in FIG. 2B.

FIG. 8. Effect of an activating antibody against TNF-α-receptor p55(Act-anti-p55) on readout patterns in multiple assay combinations.Confluent cultures of HUVEC cells were treated with TNF-α (5 ng/ml),IFN-γ (100 ng/ml), IL-1 (1 ng/ml), the combination of TNF-α+IFN-γ+IL-1,or media in the presence or absence of Act-anti-p55 (Act-anti-p55, 3μg/ml, R&D Systems). After 24 hours, cultures were washed and evaluatedfor the cell surface expression of ICAM-1 (1), VCAM-1 (2), E-selectin(3), IL-8 (4), and MIG (5) by cell-based ELISA performed as described inFIG. 1. A color-coded representation of the biomaps prepared from thedata is shown, coded as described in FIG. 2B.

FIG. 9. Effect of a soluble TNF-α-receptor p55-Fc fusion protein(p55-Fc) on the expression of readout parameters in an assay combinationcontaining an activating antibody against TNF-α-receptor p55(Act-anti-p55). Confluent cultures of HUVEC cells were treated with orwithout (Control) Act-anti-p55 in the presence or absence of p55-Fc.After 24 hours, cultures were washed and evaluated for the cell surfaceexpression of ICAM-1 (1), VCAM-1 (2), E-selectin (3), IL-8 (4), CD31(5), HLA-DR (6) and MIG (7) by cell-based ELISA performed as describedin FIG. 1. The relative expression of each parameter is shown along they-axis as average value of the OD measured at 450 nm of triplicatesamples. The mean +/−SD from triplicate samples are shown. * indicatesp<0.05 comparing results obtained with Act-anti-p55 orAct-anti-p55+p55-Fc to the Control, n=3.

FIG. 10. Effect of neutralizing antibodies against IL-1 or TNF-α on theexpression of readout parameters in the optimized assay combination ofExample 1. Confluent cultures of HUVEC cells were treated with TNF-α (5ng/ml)+IFN-γ (100 ng/ml)+IL-1 (1 ng/ml) in the presence or absence ofneutralizing antibodies to IL-1 (Anti-IL-1, 4 μg/ml, R&D Systems), TNF-α(Anti-TNF-α, μg/ml/ml, R&D Systems) or the combination. After 24 hours,cultures were washed and evaluated for the cell surface expression ofICAM-1 (1), VCAM-1 (2), E-selectin (3), IL-8 (4), CD31 (5), HLA-DR (6)and MIG (7) by cell-based ELISA performed as described in FIG. 1. A. Therelative expression of each parameter is shown along the y-axis asaverage value of the OD measured at 450 nm of triplicate samples. Themean +/−SD from triplicate samples are shown. B. A color-codedrepresentation of the biomaps prepared from the data in FIG. 12A isshown, coded as described in FIG. 2B where the control conditionincludes TNF-α+IFN-γ+IL-1.

FIG. 11 A-B. Effect of AG126 and PPM-18 on expression of readoutparameters in the optimized assay combination of Example 1. Confluentcultures of HUVEC cells were treated with TNF-α (5 ng/ml)+IFN-γ (100ng/ml)+IL-1 (1 ng/ml) in the presence or absence of AG126 (25 μM) orPPM-18 (2 μM) or the combination. Compounds were tested at the highestconcentration at which they were soluble, and/or did not result in celldeadhesion. After 24 hours, cultures were washed and evaluated for thecell surface expression of ICAM-1 (1), VCAM-1 (2), E-selectin (3), IL-8(4), CD31 (5), HLA-DR (6) and MIG (7) by cell-based ELISA performed asdescribed in FIG. 1. A color-coded representation of the biomapsprepared from the data is shown in 11B, coded as described in FIG. 2B.

FIG. 12. Confluent cultures of HUVEC cells were treated withcombinations of IL-4 (20 ng/ml), TNF-α (5 ng/ml), histamine (HIS, 10 μM)and/or base media. After 24 hours, cultures were washed and evaluatedfor the presence of ICAM-1 (1), VCAM-1 (2), E-selectin (3), IL-8 (4),CD31 (5), P-selectin (6) and Eotaxin-3 (7) by cell-based ELISA performedas described in FIG. 1. A color-coded representation of the biomapsprepared from the data is shown, coded as described in FIG. 2B. For eachparameter and assay combination, the square is colored light gray if theparameter measurement is unchanged (<20% above or below the measurementin the first assay combination (IL-4+TNF-α+HIS) or p>0.05, n=3);white/gray hatched indicates that the parameter measurement ismoderately increased (>20% but <50%); white indicates the parametermeasurement is strongly increased (>50%); black/gray hatched indicatesthat the parameter measurement is moderated decreased (>20% but <50%);black indicates that the parameter measurement is strongly decreased(>50% less than the level measured in the first assay combination).

FIG. 13. Cultures of normal human epithelial keratinocytes (NHEK) weretreated with combinations of TNF-α (50 ng/ml), IFN-γ (50 ng/ml), IL-1 (1ng/ml) and or base media. After 48 hours, cultures were washed andevaluated for the presence of MIG (1), ICAM-1 (2), CD44 (3), IL-8 (4),MIP-3alpha (5), MCP-1 (6), and E-cadherin (7) by cell-based ELISAperformed as described in FIG. 1. A color-coded representation of thebiomaps prepared from the data is shown, coded as described in FIG. 2B.For each parameter and assay combination, the square is colored lightgray if the parameter measurement is unchanged (<20% above or below themeasurement in the first assay combination (IL-1+IFN-γ) or p>0.05, n=3);white/gray hatched indicates that the parameter measurement ismoderately increased (>20% but <50%); white indicates the parametermeasurement is strongly increased (>50%); black/gray hatched indicatesthat the parameter measurement is moderated decreased (>20% but <50%);black indicates that the parameter measurement is strongly decreased(>50% less than the level measured in the first assay combination).

FIG. 14. Assay combinations containing HUVEC and T cell co-cultures.Confluent cultures of HUVEC were incubated with media (No Cells), TNF-α,(5 ng/ml), IFN-γ (100 ng/ml) or KIT255 T cells with and without IL-2 (10ng/ml) and/or IL-12 (10 ng/ml). After 24 hours cultures were washed andevaluated for the cell surface expression of ICAM-1 (1), VCAM-1 (2),E-selectin (3), IL-8 (4), CD31 (5), HLA-DR (6) and MIG (7) by cell-basedELISA performed as described in FIG. 1. The relative expression of eachparameter is shown along the y-axis as average value of the OD measuredat 450 nm.

FIG. 15. Schematic representation of retroviral vector constructs (notdrawn to scale). LTR, long terminal repeat; IRES, internal ribosomalentry site.

FIG. 16. Effect of Bcl-3 gene over-expression on readout patterns inmultiple BioMap systems of inflammation. HUVEC cells were transducedwith either Bcl-3-expressing retroviral vector (Bcl-3) or control emptyvector (control). Confluent cultures were treated with either TNF-α (5ng/ml), IL-1 (1 ng/ml), TNF-α (5 ng/ml)+IFN-γ (100 ng/ml)+IL-1 (1ng/ml), or media only (no cytokine). After 24 hours, cultures werewashed and evaluated for the cell surface expression of ICAM-1 (1),VCAM-1 (2), E-selectin (3), IL-8 (4), CD31 (5), HLA-DR (6) and MIG (7)by cell-based ELISA performed as described in FIG. 1. A color-codedrepresentation of the biomaps prepared from the data is shown, coded asdescribed in FIG. 2B.

FIG. 17. Effect of over-expression of bcl-2 and bcl-xl proteins on apanel of assay combinations. HUVEC cells were transduced with eitherBcl-2, Bcl-xL or control empty vector (control). Confluent cultures weretreated with either ceramide (10 μm), TNF-α (5 ng/ml), ceramide (10μm)+TNF-α (5 ng/ml), or media only. After 24 hours, cells were washedand evaluated for the surface expression of ICAM-1 (1), VCAM-1 (2), andMIG (3) by cell-based ELISA performed as described in FIG. 1. Cellsupernatants were collected and analyzed for the presence of lactatedehydrogenase LDH (4) by CytoTox96 assay (Promega). The level of LDH inculture supernatants from cells treated with TNF-α or ceramide +TNF-αwas increased 2-fold and 2.2-fold, respectively, over the levelsmeasured in untreated or ceramide-treated cells. A color-codedrepresentation of the biomaps prepared from the data is shown, coded asdescribed in FIG. 2B.

FIG. 18. Effect of TNF-R1-p55 antisense oligonucleotide on multipleassay combinations. Confluent cultures of HUVEC cells were transfectedwith TNF-R1 antisense or control β-globin antisense oligonucleotides,and then treated with either TNF-α (0.5 ng/ml), or IL-1 (1 ng/ml). After4 hours, cells were harvested and evaluated for the cell surfaceexpression of ICAM-1 (1), VCAM-1 (2), E-selectin (3), and CD31 (4) byflow cytometry. Flow cytometry was performed as previously described(Berg, Blood, 85:31, 1995). A color-coded representation of the biomapsprepared from the data is shown, coded as described in FIG. 2B.

DESCRIPTION OF THE SPECIFIC EMBODIMENTS

Flexible multiplex screening assays are provided for the screening andbiological activity classification of biologically active agents andgenes.

In the screening assays for the biologically active agents, the effectof altering the environment of cells in culture is tested with a panelof cells and cellular environments. The effect of the altering of theenvironment is assessed by monitoring multiple output parameters. Theresult is an analysis providing “function homology,” where comparison oftwo different environments, particularly differing by differentcompounds present in the environment, can be directly compared as totheir similarities and differences. By being able to compare the effecton a family of parameters as to the degree of change in the absence ofthe compounds, the function of the compounds can be compared, thepathways affected identified and side effects predicted.

In the screening assays for genetic agents, polynucleotides are added toone or more of the cells in a panel in order to alter the geneticcomposition of the cell. The output parameters are monitored todetermine whether there is a change in phenotype affecting particularpathways. In this way, genetic sequences are identified that encode oraffect expression of proteins in pathways of interest, particularlypathways associated with aberrant physiological states.

Assay combinations, usually employing cell cultures, are provided thatsimulate physiological cell states of interest, particularlyphysiological cell states in vivo, usually using the same type of cellsor combinations of cells. These cell cultures are created by theaddition of a sufficient number of different factors to provoke aresponse that simulates cellular physiology of the state of interest andto allow for the status of cells in culture to be determined in relationto a change in an environment. The state of interest will normallyinvolve a plurality of pathways where the pathways regulate a pluralityof parameters or markers identifying a phenotype associated with thestate of interest.

The phenotype can be generated by including a plurality of factors thatinduce pathways affecting the production of the phenotype by the up ordown regulation of formation of the parameters as detectable products ormay be based on the nature of the cell, e.g. neoplastic primary cells,cell lines, etc., where the factors enhance the response of the cells invitro to more closely approximate the response of interest. The factorsare naturally occurring compounds, e.g. known compounds that havesurface membrane receptors and induce a cellular signal that results ina modified phenotype, or synthetic compounds that mimic the naturallyoccurring factors. In some instances, the factors will actintracellularly by passing through the cell surface membrane andentering the cytosol with binding to components in the cytosol, nucleusor other organelle. In providing the environment by use of the factorsor mimetics, one provides the activities of the factors to theenvironment, using the naturally occurring factors or their mimetics. Inreferring to factors, it is understood that it is the activities of thefactors that are of interest and not necessarily a particular naturallyoccurring factor itself.

The nature and number of parameters measured generally reflects theresponse of a plurality of pathways. The subject approach provides forrobust results having enhanced predictability in relation to thephysiological state of interest. The results may be compared to thebasal condition and/or the condition in the presence of one or more ofthe factors, particularly in comparison to all of the factors used inthe presence and absence of agent. The effects of different environmentsare conveniently provided in biomaps, where the results can bemathematically compared.

For screening assays with genetic agents, the same approach will be usedas above. The genetic agents are added to cells, which are placed in amedium where one or more factors may be present to provide a desiredenvironment, namely an environment of interest, such as a physiologicalenvironment involved with an aberrant, e.g. diseased, state. Parametersassociated with the pathways related to the physiological state aremonitored. Where the parameters show a pattern indicating the up or downregulation of a pathway, the genetic agent is deduced to encode oraffect the expression of a member of the pathway. In this way one candetermine the role a gene plays in the physiological state of interest,as well as define targets for therapeutic application.

Once biomaps have been prepared for pathways and/or environments ofinterest, assays may be carried out with or without the factors. Knowingthe variation in parameters with individual factors and differentcombinations of factors, one can compare the effect of an agent on acell culture by measuring parameters that have been previously measuredin different assay combinations. The observed effect of the agent on thelevels of the different parameters may then be correlated with theobserved effect of the factors and combinations of factors in the biomapdataset.

Numerous factors are known that induce pathways in cells that areresponsive to the factor. For the most part, factors bind to cellsurface receptors, although other receptors may be involved, such asreceptors at the nuclear membrane. In addition, where a factor is ableto penetrate the surface membrane, through passive or active transportor through endocytosis, the factor may bind to components of themembrane, cytosol or an organelle, e.g. nucleus. It has now been foundthat by using a combination of multiple factors to provoke a cellularresponse, and multiple parameters associated with a physiological stateof interest, one can investigate multiple individual cellularphysiological pathways and simulate the physiological response to achange in environment and obtain greater predictability as to the waythe physiological situation will respond to the change in environment.The subject screening of physiologically active compounds provides forgreater assurance of the effect of the change of environment in thephysiological circumstances in which the change is to occur.

Multiple factors are employed, which provides a robust simulation of thephysiological state or physiologic pathways of interest and allows forreliable responses that can be correlated with in vivo cellularresponses. Alternatively, factors can be employed that simulate theenvironment of the cells in vivo (particularly a living animal, but maybe cells, tissue, organelles, etc.), so that the cell physiology of thecells in culture more closely approximates the cell physiology in vivo.

Combinations of factors are employed where pathways involved with aparticular cellular status are active, resulting in the modulation ofthe formation of various products, such as RNA, e.g. mRNA, tRNA, etc.,proteins, metabolites, functional states of proteins, etc., wheredifferent products are associated with different pathways. All of theseproducts are detectable and can be analyzed by appropriate assays.Specific products are selected for measurement, usually avoidingproducts that give redundant information, e.g. that are commonlyregulated. The results obtained from individual assay combinations maythen be compiled. These results are compared or normalized with thecontrol state, which can be the cells in an appropriate medium with orwithout exogenous factors other than the test agent, or the stimulatedculture, which is the cells in the absence of the agent, but in samemedium with the factors that induce the cells in culture to simulatecells in a complex environment that occurs in vivo. For the most part,the control state will be the cell culture with the same factors andmeasuring the same parameters as the test state comprising the agent.

In referring to simulation to a physiological state, the simulation willusually include at least three different regulated features (parameters)shared with in vivo cell counterparts in normal or diseased states.Alternatively, the simulation may include a cell culture system thatallows discrimination of modifications in at least three differentsignaling pathways or cell functions operative in vivo under conditionsof interest.

The results can be entered into a data processor to provide a biomapdataset. Algorithms are used for the comparison and analysis of biomapsobtained under different conditions. The effect of factors and agents isread out by determining changes in multiple parameters in the biomap.The biomap will include the results from assay combinations with theagent(s), and may also include one or more of the control state, thesimulated state, and the results from other assay combinations usingother agents or performed under other conditions. For rapid and easycomparisons, the results may be presented visually in a graph of abiomap, and can include numbers, graphs, color representations, etc.

Biomap

The biomap is prepared from values obtained by measuring parameters ormarkers of the cells in the presence and absence of different factors,as well as comparing the presence of the agent of interest and at leastone other state, usually the control state, which may include the statewithout agent or with a different agent. The parameters include cellularproducts or epitopes thereof, as well as functional states, whose levelsvary in the presence of the factors. Desirably, the results arenormalized against a standard, usually a “control value or state,” toprovide a normalized data set. Values obtained from test conditions canbe normalized by subtracting the unstimulated control values from thetest values, and dividing the corrected test value by the correctedstimulated control value. Other methods of normalization can also beused; and the logarithm or other derivative of measured values or ratioof test to stimulated or other control values may be used. Data isnormalized to control data on the same cell type under controlconditions, but a biomap may comprise normalized data from one, two ormultiple cell types and assay conditions.

By referring to a biomap is intended that the dataset will comprisevalues of the levels of at least two sets of parameters obtained underdifferent assay combinations. Depending on the use of the biomap, thebiomap may also include the parameter values for each the factorsincluded in the assay combination, individually and/or together withfewer than the entire assay combination. Compilations of biomaps aredeveloped that provide the values for a sufficient number of alternativeassay combinations to allow comparison of values obtained where factorshave not been added. While such an assay can be less predictive of invivo conditions, in many situations it can suffice to provide a rapid,inexpensive screen providing useful data. For example, if one wereinterested in side effects of a candidate compound, by using a cellculture that is in a basal state, one could evaluate whether thecandidate compound produced an aberrant state, e.g. normal as comparedto inflamed. The parameter values are usually created electronically andstored in a data processor for comparison with other biomaps anddatabases compiled from the biomaps.

A graph of a biomap can be presented visually as numerical values,symbols, color gradations, or the like, indicating the parameter values.The graph is conveniently presented where color and/or design provide anindication of the level of the particular marker. The indicators may bevertical or horizontal as to the individual markers and the assaycombinations, so that by looking at the graph, one can immediatelycompare the levels of the different markers for each of the combinationsand discern patterns related to the assay combinations and thedifferences between assay combinations. In this way, one can rapidlyrelate different candidate pharmacologic agents, the pathways theyaffect and their efficacy in modulating the individual pathways.

Optionally, a biomap can be annotated to indicate information about thesources of information for the dataset. Annotations may include, forexample, the number of assay conditions in a panel (n); controls usedfor normalization (N); parameters (P), which may be designated for thenumber and identity of the parameters; environmental changes, such asthe addition of factors and/or agents or a change in the physicalconditions (V); cell type (C); and the like. The annotation may furtherspecify specific factors or conditions present in one of the assaycombinations, e.g. n1, n2, n3, etc., where the presence of factors inthe assay combination is designated (F), temperature may be designated(T), pH, etc. The parameters may also be designated in this as, e.g.P1=ICAM-1, P2=VCAM-1, P3=E-selectin, etc. Written out, the annotationmay be set forth as: (v) B {n; N; P; C; F}.

As an example: a biomap is produced from monitoring endothelial cellsfor four parameters in four assay combinations. The assay combinationsinclude a basal control, a stimulated control, and a control where thepathway of interest is blocked by the addition of neutralizing antibody.The compound being tested is an NSAID. The biomap (B) may be annotatedas:

-   -   (NSAID) B {n=1-4; N=basal/stim.; P=1-4; C=endothelial;        F_((n4))=neut. Ab}

A database of biomaps can be compiled from sets of experiments, forexample, a database can contain biomaps obtained from a panel of assaycombinations, with multiple different environmental changes, where eachchange can be a series of related compounds, or compounds representingdifferent classes of molecules. In another embodiment, a databasecomprises biomaps from one compound, with multiple different cellpanels.

Mathematical systems can be used to compare biomaps, and to providequantitative measures of similarities and differences between them. Forexample, the biomaps in the database can be analyzed by patternrecognition algorithms or clustering methods (e.g. hierarchical ork-means clustering, etc.) that use statistical analysis (correlationcoefficients, etc.) to quantify relatedness of biomaps. These methodscan be modified (by weighting, employing classification strategies,etc.) to optimize the ability of a biomap to discriminate differentfunctional effects. For example, individual parameters can be given moreor less weight when analyzing the dataset of the biomap, in order toenhance the discriminatory ability of the biomap. The effect of alteringthe weights assigned each parameter is assessed, and an iterativeprocess is used to optimize pathway or cellular function discrimination.

Assay Combination

Cells for use in the assays of the invention can be an organism, asingle cell type derived from an organism, or can be a mixture of celltypes, as is typical of in vivo situations, but may be the differentcells present in a specific environment, e.g. vessel tissue, liver,spleen, heart muscle, brain tissue, etc. The cells will usually be ofthe same type as the cells of the physiologic conditions, sharing atleast a partially common phenotype. For example, both the culture andthe in vivo physiologic condition could involve T-lymphocytes, where theculture would involve a T-lymphocyte cell line or primary T-lymphocyte.In some instances the cells in the culture or assay combination may besubstantially different from the cells of the physiologic state ofinterest. Where it is known or can be shown that the pathways of thecells in culture are paradigmatic of the pathways of the cells ofinterest, the cells in culture may be selected for reasons ofconvenience, that a body of data has been built up with these cells,easy growth and maintenance, the use by others allowing for moreaccurate comparisons of the results, etc.

Of particular interest are primary cells that can be used in a culture,where the primary cells of interest are, in effect, synchronized intheir phenotype, by the use of the factors. When the cells are not insynchrony, an average value will be obtained. The culture conditionswill include the presence of factors that provide for the desiredphysiologic state, including the desired phenotype, but may also bevaried, for example, as to temperature, pH, presence of other celltypes, and the like. Each combination of cell(s) and culture conditionsprovides one “assay combination”, which will generate a set of parameterreadouts. In a typical screen, a panel of one or more assay combinationsis used for each compound to be tested. For each assay combination, aset of parameter readouts will be obtained in the presence of an agentthat is being tested. These readouts will be compared to readouts of anassay combination lacking the agent, which may be performedcontemporaneously or may be performed at another time, either before orafter the assay combination with the agent of interest. As indicatedabove, the comparison may be with the same type of cells in the absenceof the factors, in the presence of the factors, or multiple stimulatingor inhibiting factors or in the presence of a different agent or othercondition that serves to provide a meaningful comparison.

Single cell types are of interest for many screening applications, andin individual assay combinations will be provided with factors thatinduce the desired phenotype. The factors may be the products of othercell types, for example, expressed proteins associated with a disease,may be compounds that simulate naturally occurring factors, may besurface membrane proteins free of the membrane or as part of microsomes,or other reagent that induces the appropriate pathway to aid in thesimulation of the phenotype or provides the appropriate environment tosimulate the physiological condition. The factors (including mimeticsthereof) may be added individually or in combination, from feeder cells,may be added as a bolus or continuously, where the factor is degraded bythe culture, etc. Illustrative naturally occurring factors includecytokines, soluble receptors, hormones, prostaglandins, steroids, etc,that may be isolated from natural sources or produced by recombinanttechnology or synthesis, compounds that mimic the action of othercompounds or cell types, e.g. an antibody which acts like a factor ormimics a factor, such as synthetic drugs that act as ligands for targetreceptors. For example, in the case of the T cell receptor, the actionof an oligopeptide processed from an antigen and presented by anantigen-presenting cell, etc. can be employed. Where a family of relatedfactors are referred to with a single designation, e.g. IL-1, VEGF, IFN,etc., in referring to the single description, any one or some or all ofthe members of the group are intended, where the literature will beaware of how the factors are to be used in the context of the assaycombination.

The assay combinations find use in investigating complex states ofcells, frequently resulting from cellular interactions, which mayfrequently involve at least about two, frequently three, or moredifferent cell types and/or will involve a plurality of soluble factorsthat are present in a physiological fluid, particularly as the result ofa physiological event, e.g. infection, neoplasia, autoimmune, etc. thatis, frequently involving more than one cell type and more than onefactor. The measured parameters may be obtained from one or more of thecell types. The cells in the assay combination, either one or up to eachof the different cell types, can have identifying characteristicsallowing them to be distinguished during analysis. Various techniquesmay be employed to identify the cells in the assay combination foranalysis of the parameters of interest.

Conditions of interest include inflammatory processes that occur inresponse to infection, trauma, etc., autoimmune diseases, such asdiabetes, lupus, arthritis, etc., cardiovascular diseases, such asstroke, atherosclerosis, etc., neoplasia, hyperplasia, addiction,infection, obesity, cellular degeneration, apoptosis, senescence,differentiation, and the like.

Multifactorial, usually involving multicellular, assay combinations, mayreflect many of the conditions indicated above, such as inflammatoryprocesses; autoimmune diseases; cardiovascular diseases; tumors, etc.That is, a multiplicity of factors are employed to influence a pluralityof cellular pathways and a multiplicity of parameters are measured thatreflect the status of the pathways. Degenerative diseases, includingaffected tissues and surrounding areas, may be exploited to determineboth the response of the affected tissue, and the interactions withother cell types or other parts of the body.

The invention is suitable for use with any cell type, including primarycells, normal and transformed cell lines, transduced cells and culturedcells. The present invention is suitable for use with single cell typesor cell lines; or combinations thereof. In assays the cultured cells maymaintain the ability to respond to stimuli that elicit a response intheir naturally occurring counterparts. Cultured cells may have gonethrough up to five passages or more, sometimes 10 passages or more.These may be derived from all sources, particularly mammalian, and withrespect to species, e.g., human, simian, rodent, etc., although othersources of cells may be of interest in some instances, such as plant,fungus, etc.; tissue origin, e.g. heart, lung, liver, brain, vascular,lymph node, spleen, pancreas, thyroid, esophageal, intestine, stomach,thymus, etc.

In addition, cells that have been genetically altered, e.g. bytransfection or transduction with recombinant genes or by antisensetechnology, to provide a gain or loss of genetic function, may beutilized with the invention. Methods for generating genetically modifiedcells are known in the art, see for example “Current Protocols inMolecular Biology”, Ausubel et al., eds, John Wiley & Sons, New York,N.Y., 2000. The genetic alteration may be a knock-out, usually wherehomologous recombination results in a deletion that knocks outexpression of a targeted gene; or a knock-in, where a genetic sequencenot normally present in the cell is stably introduced.

A variety of methods may be used in the present invention to achieve aknock-out, including site-specific recombination, expression ofanti-sense or dominant negative mutations, and the like. Knockouts havea partial or complete loss of function in one or both alleles of theendogenous gene in the case of gene targeting. Preferably expression ofthe targeted gene product is undetectable or insignificant in the cellsbeing analyzed. This may be achieved by introduction of a disruption ofthe coding sequence, e.g. insertion of one or more stop codons,insertion of a DNA fragment, etc., deletion of coding sequence,substitution of stop codons for coding sequence, etc. In some cases theintroduced sequences are ultimately deleted from the genome, leaving anet change to the native sequence.

Different approaches may be used to achieve the “knock-out”. Achromosomal deletion of all or part of the native gene may be induced,including deletions of the non-coding regions, particularly the promoterregion, 3′ regulatory sequences, enhancers, or deletions of gene thatactivate expression of the targeted genes. A functional knock-out mayalso be achieved by the introduction of an anti-sense construct thatblocks expression of the native genes (for example, see Li and Cohen(1996) Cell 85:319-329). “Knock-outs” also include conditionalknock-outs, for example where alteration of the target gene occurs uponexposure of the animal to a substance that promotes target genealteration, introduction of an enzyme that promotes recombination at thetarget gene site (e.g. Cre in the Cre-lox system), or other method fordirecting the target gene alteration.

The genetic construct may be introduced into tissues or host cells byany number of routes, including calcium phosphate transfection, viralinfection, microinjection, or fusion of vesicles. Jet injection may alsobe used for intramuscular administration, as described by Furth et al.(1992), Anal Biochem 205:365-368. The DNA may be coated onto goldmicroparticles, and delivered intradermally by a particle bombardmentdevice, or “gene gun” as described in the literature (see, for example,Tang et al. (1992), Nature 356:152-154), where gold microprojectiles arecoated with the DNA, then bombarded into cells.

A number of selection systems may be used for introducing the geneticchanges, including but not limited to the herpes simplex virus thymidinekinase (Wigler, et al., 1977, Cell 11:223), hypoxanthine-guaninephosphoribosyltransferase (Szybalska & Szybalski, 1962, Proc. Natl.Acad. Sci. USA 48:2026), and adenine phosphoribosyltransferase (Lowy, etal., 1980, Cell 22:817) genes can be employed in tk.sup.-, hgprt.sup.-or aprt.sup.-cells, respectively. Also, antimetabolite resistance can beused as the basis of selection for the following genes: dhfr, whichconfers resistance to methotrexate (Wigler, et al., 1980, Natl. Acad.Sci. USA 77:3567; O'Hare, et al., 1981, Proc. Natl. Acad. Sci. USA78:1527); gpt, which confers resistance to mycophenolic acid (Mulligan &Berg, 1981, Proc. Natl. Acad. Sci. USA 78:2072); neo, which confersresistance to the aminoglycoside G-418 (Colberre-Garapin, et al., 1981,J. Mol. Biol. 150:1); and hygro, which confers resistance to hygromycin(Santerre, et al., 1984, Gene 30:147).

The literature has ample evidence of cells involved with manyphysiological states of interest, factors involved in inducing changesin the phenotype, and markers resulting from the interaction between thefactors and the target cells affected by the factors. Primary cells fortissues of interest are readily available commercially and can beexpanded as required. Biopsies can serve as a source of cells, bothnormal and diseased cells.

Cell types that can find use in the subject invention, includeendothelial cells, muscle cells, myocardial, smooth and skeletal musclecells, mesenchymal cells, epithelial cells; hematopoietic cells, such aslymphocytes, including T-cells, such as Th1 T cells, Th2 T cells, Th0 Tcells, cytotoxic T cells; B cells, pre-B cells, etc.; monocytes;dendritic cells; neutrophils; and macrophages; natural killer cells;mast cells; etc.; adipocytes, cells involved with particular organs,such as thymus, endocrine glands, pancreas, brain, such as neurons,glia, astrocytes, dendrocytes, etc. and genetically modified cellsthereof. Hematopoietic cells will be associated with inflammatoryprocesses, autoimmune diseases, etc., endothelial cells, smooth musclecells, myocardial cells, etc. may be associated with cardiovasculardiseases; almost any type of cell may be associated with neoplasias,such as sarcomas, carcinomas and lymphomas; liver diseases with hepaticcells; kidney diseases with kidney cells; etc.

The cells may also be transformed or neoplastic cells of differenttypes, e.g. carcinomas of different cell origins, lymphomas of differentcell types, etc. The American Type Culture Collection (Manassas, Va.)has collected and makes available over 4,000 cell lines from over 150different species, over 950 cancer cell lines including 700 human cancercell lines. The National Cancer Institute has compiled clinical,biochemical and molecular data from a large panel of human tumor celllines, these are available from ATCC or the NCl (Phelps et al. (1996)Journal of Cellular Biochemistry Supplement 2.:32-91). Included aredifferent cell lines derived spontaneously, or selected for desiredgrowth or response characteristics from an individual cell line; and mayinclude multiple cell lines derived from a similar tumor type but fromdistinct patients or sites.

In addition, cells may be environmentally induced variants of singlecell lines: e.g., a responsive cell line, such as a transformedendothelial cell line, split into independent cultures and grown underdistinct conditions, for example with or without cytokines, e.g. IL-1,with or without IFN-(, with or without endothelial growth factors, andin the presence or absence of other cytokines or combinations thereof.Each culture condition then induces specific distinctive changes in thecells, such that their subsequent responses to an environment change isdistinct, yielding a distinctive biomap. Alternatively, the cells may betransduced or otherwise genetically modified cells.

The term “environment,” or “culture condition” encompasses cells, medir,factors, time and temperature. Environments may also include drugs andother compounds, particular atmospheric conditions, pH, saltcomposition, minerals, etc. The conditions will be controlled and thebiomap will reflect the similarities and differences between each of theassay combinations involving a different environment or culturecondition.

Culture of cells is typically performed in a sterile environment, forexample, at 37° C. in an incubator containing a humidified 92-95%air/5-8% CO₂ atmosphere. Cell culture may be carried out in nutrientmixtures containing undefined biological fluids such as fetal calfserum, or media which is fully defined and serum free.

Some preferred environments include environments that discriminate oremphasize cell or tissue states associated with pathology in one or morediseases, for example, Th1 versus Th2 polarization of effector T cells;prothrombotic; inflammatory (e.g. NFκB, upregulated TNF-β cytokineproduction, downregulated IL-10, TGFα, etc.; dysregulated proliferation(neoplasia); angiogenesis; etc.) Environments that facilitatediscrimination of specific signaling pathways implicated in diseasestates are also of interest, e.g. NFκB, classic Th1 or Th2 inductionenvironments, etc.

Physiologically Relevant Assay Combination

Cell culture conditions that reflect multiple aspects of a physiologicalstate are termed herein a “representation” or “simulation” of thecondition of interest, normally the in vivo condition. There are severalimportant, and inter-related variables to be considered when setting upthe in vitro counterpart conditions. These include the types of cellsthat are involved, the media employed, the conditions for the culture,the presence of biologically active factors in the cell's physiologicalmilieu; and the phenotype of the cells, which may be determined both inthe absence and presence of pharmacologic agents or for geneticallymodified and unmodified cells.

While a single cell can find use in an assay combination, normally thenumber of cells will be at least 10², usually at least 10³, andconveniently are grown to confluence.

In many cases the literature has sufficient information to establishassay combinations to provide a useful biomap. Where the information isnot available, by using the procedures described in the literature foridentifying markers for diseases, using subtraction libraries,microarrays for RNA transcription comparisons, proteomic or immunologiccomparisons, between normal and cells in the physiologic state ofinterest, using knock-out and knock-in animal models, using modelanimals that simulate the physiological state, by introducing cells ortissue from one species into a different species that can accept theforeign cells or tissue, e.g. immunocompromised host, one can ascertainthe endogenous factors associated with the physiologic state and themarkers that are produced by the cells associated with the physiologicstate.

Once a biomap of the components of the assay combination have been shownto be relevant to a physiologic state of interest, biomap analysis canbe used to optimize cell culture conditions that more accuratelyrepresent or simulate such physiologic state in vivo, e.g. in diseasestates of interest. That is, the values for various parameters fromcells in vivo can be used as a template for the process of representingthose same cells in culture. Additional markers can be deduced and addedas a marker to the map. The greater the number of individual markersthat vary independently of each other, the more robust the biomap. Byoptimizing culture conditions and selection of parameters, a biomap froma cell panel in vitro can be made representative of an in vivophenotype. In other words, in vitro culture conditions can bemanipulated in order to generate cells having a biomap that mimics theparameter readout obtained from similar cells in a specific in vivostate of interest. There will usually be employed for generation of thebiomap at least about three parameter or marker readouts, morefrequently 4 or more, generally not more than 20, more usually not morethan about 10, that have similar response patterns in the in vitro andin vivo conditions. A larger number of shared parameters indicates agreater relevance of the cultured cells for the disease state and willusually be indicative of a plurality of pathways associated with thephysiologic state in vivo. The parameters selected will permit thereadout of at least 2, more usually, at least about 3 or more cellpathways.

If desired, the parameters of the biomap can be optimized by obtainingbiomap parameters within an assay combination or panel of assaycombinations using different sets of readout, and using patternrecognition algorithms and statistical analyses to compare and contrastdifferent biomaps of different parameter sets. Parameters are selectedthat provide a biomap that discriminates between changes in theenvironment of the cell culture known to have different modes of action,i.e. the biomap is similar for agents with a common mode of action, anddifferent for agents with a different mode of action. The optimizationprocess allows the identification and selection of a minimal set ofparameters, each of which provides a robust readout, and that togetherprovide a biomap that enables discrimination of different modes ofaction of stimuli or agents. The iterative process focuses on optimizingthe assay combinations and readout parameters to maximize efficiency andthe number of signaling pathways and/or functionally different cellstates produced in the assay configurations that can be identified anddistinguished, while at the same time minimizing the number ofparameters or assay combinations required for such discrimination.

There are established protocols for the culture of diverse cell typesthat reflect their in vivo counterparts. Protocols may require the useof special conditions and selective media to enable cell growth orexpression of specialized cellular functions. Such methods are describedin the following: Animal Cell Culture Techniques (Springer Lab Manual),Clynes (Editor), Springer Verlag, 1998; Animal Cell Culture Methods(Methods in Cell Biology, Vol 57, Barnes and Mather, Eds, AcademicPress, 1998; Harrison and Rae, General Techniques of Cell Culture(Handbooks in Practical Animal Cell Biology), Cambridge UniversityPress, 1997; Endothelial Cell Culture (Handbooks in Practical AnimalCell Biology), Bicknell (Editor), Cambridge University Press, 1996;Human Cell Culture, Cancer Cell Lines Part I: Human Cell Culture,Masters and Palsson, eds., Kluwer Academic Publishers, 1998; Human CellCulture Volume II—Cancer Cell Lines Part 2 (Human Cell Culture Volume2), Masters and Palsson, eds., Kluwer Academic Publishers, 1999; Wilson,Methods in Cell Biology: Animal Cell Culture Methods (Vol 57), AcademicPress, 1998; Current Protocols in Immunology, Coligan et al., eds, JohnWiley & Sons, New York, N.Y., 2000; Current Protocols in Cell Biology,Bonifacino et al., eds, John Wiley & Sons, New York, N.Y., 2000.

The cell surface expression of various surface and intracellularmarkers, including protein, lipid, nucleic acid, e.g. genetic markers,and carbohydrate is known for a large number of different types ofcells, and can be used as a reference for establishing the exactphenotype of cells in vivo; for determining whether that same phenotypeis present in the cultured cells, for determining the effect of anagent, particularly a pharmacologic agent, on the cells, and the like.The manner in which cells respond to an agent, particularly apharmacologic agent, including the timing of responses, is an importantreflection of the physiologic state of the cell.

For example, one might determine by histologic and antibody staining thephenotypes of cells in a biopsy sample from a chronically inflamedtissue. This information would be used to determine the types of cellsthat are present, and their physiologic state, e.g. activated,responding to a cytokine, etc. and their environment, e.g. presence ofcytokines. A corresponding assay combination is then established fromthe information, which provides the relevant cells in the appropriatestate. A biomap is then derived from the assay combination and controlsto provide an in vitro culture as an appropriate surrogate for the invivo state. Usually, an in vivo response will match multiple parametervalues (i.e. up or down regulation of parameters) to similarlyresponding cells in a “representative” assay combination.

As indicated previously, for many physiologic states, cell types,factors and markers are known. In addition, concentrations having thedesired induction of change in phenotype are also known. Also asdiscussed above, these conditions can be further optimized by makingvariations in concentrations, ratios, choice of markers, etc. to providemore accurate simulations of the naturally occurring physiologicalstate. Assay combinations that represent in vivo states may go throughan iterative process. Based on the information in the literature orindependently derived, one devises an initial set of culture conditions,which includes combinations of known biologically active factors.Depending on the desired biomap, these factors can include cytokines,chemokines, and other factors, e.g. growth factors, such factors includeGM-CSF, G-CSF, M-CSF, TGF, FGF, EGF, TNF-∀, GH, corticotropin,melanotropin, ACTH, etc., extracellular matrix components, surfacemembrane proteins, such as integrins and adhesins, and other componentsthat are expressed by the targeted cells or their surrounding milieu invivo. Components may also include soluble or immobilized recombinant orpurified receptors, or antibodies against receptors or ligand mimetics.

For cells, either primary cells or cell lines, that have the appropriatephenotype, e.g. neoplastic cells, factors will be used to provide anenvironment that simulates the environment of the neoplastic cells invivo Depending on the type of cancer, the cancer cells will be perfusedwith different factors based on the different cells in the environmentof the tumor, as well as other factors in the blood induced by factorssecreted by the neoplastic cells. Since the physiology of the cells isinfluenced by these factors, which in turn will influence the regulationof the parameters to be measured, providing these factors enhances theapproximation of the cells in culture to the cells in vivo, providingfor a more accurate readout of the effect of an agent on the cells. Manyof these factors will be the same factors described above, butadditional factors include factors associated with angiogenesis, such asangiogenin, angiopoietin-1, HGF, PDGF, TNF-α, VEGF, IL-1, IL-4, IL-6,IL-8 and fibronectin.

An initial set of readout parameters is selected, which normallyincludes parameters that are differentially produced, expressed,modulated or indirectly influenced in response to one or more of thecomponents included in the environment. These parameters normallyinclude molecules of functional importance to the cell and which arerelevant to the state of interest. The readout response of cells ismeasured in response to a defined agent, usually the addition of apharmacologic agent, although in some instances a targeted alteration ingenotype or change in environment may be involved. The resulting biomap(normalized set of parameter values) comprising the presence andrelative amount of the markers will simulate the biomap of the relevantcells in vivo. The assay conditions used to generate the biomap may befurther refined to most closely match the biomap of the cells in vivo inthe physiologic state of interest or mimic at least about 3 features ofinterest of such cells in vivo.

The same pattern of factors and parameters can be used with geneticallymodified cells, where the assay combination has the genetically modifiedcell as its variable. The genetically modified cells are scored forchanges in parameters, as compared to the genetically unmodified cells.The results are used to develop a biomap, where the biomap of thegenetically modified cell can be compared to one or the other or both ofother genetically modified cells and assay combinations involvingexogenous agents. The compiled database of biomaps can include bothbiomaps of genetic modifications, and biomaps for the effects of othercompounds. The biomaps provide identification of the pathways involved,the relationship of the activities of exogenous agents to genes, and howthe cell modifies its biology in relation to these changes.

Panels

For the most part, the biomap dataset will comprise data from a panel ofassay combinations. The panel will be related to the purpose of thebiomap and may include not only the information that has been developedsubstantially concurrently with the study, but also information that hasbeen previously developed under comparable conditions. In one embodimentof the invention, a panel is comprised of at least one assay combinationthat provides for a representation of an in vivo state of interest,while other assay combinations in the panel are variants thereof.Frequently a panel will be used that is comprised of at least one assaycombination that provides for simulation of multiple pathways ofinterest, while other assay combinations in the panel are variantsthereof. In other embodiments, a panel may be comprised of multiple,different, in vivo representations; or multiple different environmentalconditions designed to stimulate multiple cell functions and pathways.The number of combinations in a panel may vary with the particular use.For example, the minimum number of assay combinations will be two for apanel for initial screening that would comprise a single assaycombination. A panel for determining how a compound affects multiplecellular pathways or functional cell responses will usually comprise aplurality of assay combinations, usually at least about 3, more usuallyat least about 6, frequently at least about 10, and may comprise as manyas 20 or more unique assay combinations. A panel for characterizing themechanism of action of an active compound will usually comprise aplurality of assay combinations, usually at least about 4, more usuallyat least 6, frequently at least about 10 and may be as many as 20 ormore unique combinations.

Desirably, a panel will comprise at least one assay combination thatrepresents a basal or normal physiological state of the cell ofinterest, which may have been developed prior to the particular biomapor as part of an assay series, or a state in the presence of thefactors. Assay panels used in the screening methods of the invention cancomprise one or more assay combinations that provide a cultured cellcounterpart to an in vivo condition of interest, where the in vivocondition will be the normal state of a cell of interest, a cell in astate associated with disease, a state associated with an immuneresponse, an infected state, an inflammatory state, a neoplastic state,and the like. Assay panels can also comprise one or more assaycombinations designed to allow discrimination of multiple cellularpathways or functional responses of interest, e.g. because of theirparticipation in physiologic states in vivo.

In one embodiment, the panel of cells and culture conditions includesvariants of representative culture condition(s), where single specificchanges are made in order to expand the biomap dataset, e.g. byproviding combinatorial subsets of factor combinations in differentculture wells, provision of known drugs in the culture medium, utilizingcell variants comprising targeted genetic changes, etc.

In another embodiment, the panel comprises culture conditions wheremultiple specific changes are made simultaneously to the representativeenvironment, e.g. two or more changes, usually not more than about 6,more usually not more than about 4. Such changes are associated with theadditional information that is engendered by the indicated variations.The variations can include the addition of known inhibitors of specificpathways. Where the presence of the inhibitor and the candidate drugresult in no change in the modulation of the markers as compared to theabsence of the candidate drug, then the candidate drug is in the samepathway inhibited by the inhibitor and the candidate drug will usuallybe at or upstream from the site of intervention of the inhibitor in thepathway. Where a different result is obtained with the presence of thecandidate drug, then it is assumed that the candidate drug acts on adifferent pathway or may act downstream from the inhibitor in the samepathway.

Taking as an example the investigation of an inflammatory response,included in a panel can be (i) an assay combination that isrepresentative of endothelial cells responding to the set ofpro-inflammatory cytokines produced by activated monocytes; (ii) acombination that is representative of these same cells in the presenceof an anti-inflammatory drug; (iii) a basal assay combination in theabsence of proinflammatory cytokines; and (iv) variant assaycombinations that lack specific cytokines or subsets of cytokines; etc.

Parameters

Parameters are quantifiable components of cells, particularly componentsthat can be accurately measured, desirably in a high throughput system.A parameter can be any cell component or cell product including cellsurface determinant, receptor, protein or conformational orposttranslational modification thereof, lipid, carbohydrate, organic orinorganic molecule, nucleic acid, e.g. mRNA, DNA, etc. or a portionderived from such a cell component or combinations thereof. While mostparameters will provide a quantitative readout, in some instances asemi-quantitative or qualitative result will be acceptable. Readouts mayinclude a single determined value, or may include mean, median value orthe variance, etc. Characteristically a range of parameter readoutvalues will be obtained for each parameter from a multiplicity of thesame assay combinations, usually at least about 2 of the same assaycombination will be performed to provide a value. Variability isexpected and a range of values for each of the set of test parameterswill be obtained using standard statistical methods with a commonstatistical method used to provide single values.

Markers are selected to serve as parameters based on the followingcriteria, where any parameter need not have all of the criteria: theparameter is modulated in the physiological condition that one issimulating with the assay combination; the parameter is modulated by afactor that is available and known to modulate the parameter in vitroanalogous to the manner it is modulated in vivo; the parameter has arobust response that can be easily detected and differentiated and isnot too sensitive to concentration variation, that is, it will notsubstantially differ in its response to an over two-fold change; theparameter is secreted or is a surface membrane protein or other readilymeasurable component; the parameter desirably requires not more than twofactors to be produced; the parameter is not co-regulated with anotherparameter, so as to be redundant in the information provided; and insome instances, changes in the parameter are indicative of toxicityleading to cell death. The set of parameters selected is sufficientlylarge to allow distinction between reference patterns, whilesufficiently selective to fulfill computational requirements.

For each assay combination, certain parameters will be functionallyrelevant and will be altered in response to test or reference agents orconditions, while other parameters may remain static in that particularcombination. Biomaps will generally comprise only functionally relevantparameter information, although a static parameter may serve as aninternal control. A typical biomap will comprise data from at least 3functionally relevant parameters, more usually at least about 5functionally relevant parameters, and may include 10 or morefunctionally relevant parameters, usually not more than about 30, moreusually not more than about 20, parameters. In analyzing the data fromthe biomap, all of the parameters need not be weighed equally. Thoseparameters that are closely functionally associated with the diseasestate or pathophysiologic response, and/or with modulation of cellpathways of interest may be given greater weight in evaluating acandidate drug or a readout, as compared to other parameters that aresuggestive, but do not have as strong an association.

Parameters of interest include detection of cytoplasmic, cell surface orsecreted biomolecules, frequently biopolymers, e.g. polypeptides,polysaccharides, polynucleotides, lipids, etc. Cell surface and secretedmolecules are a preferred parameter type as these mediate cellcommunication and cell effector responses and can be more readilyassayed. In one embodiment, parameters include specific epitopes.Epitopes are frequently identified using specific monoclonal antibodiesor receptor probes. In some cases the molecular entities comprising theepitope are from two or more substances and comprise a definedstructure; examples include combinatorially determined epitopesassociated with heterodimeric integrins. A parameter may be detection ofa specifically modified protein or oligosaccharide, e.g. aphosphorylated protein, such as a STAT transcriptional protein; orsulfated oligosaccharide, or such as the carbohydrate structure SialylLewis x, a selectin ligand. The presence of the active conformation of areceptor may comprise one parameter while an inactive conformation of areceptor may comprise another, e.g. the active and inactive forms ofheterodimeric integrin α_(M)β₂ or Mac-1.

A parameter may be defined by a specific monoclonal antibody or a ligandor receptor binding determinant. Parameters may include the presence ofcell surface molecules such as CD antigens (CD1-CD247), cell adhesionmolecules including α₄β₇ and other integrins, selectin ligands, such asCLA and Sialyl Lewis x, and extracellular matrix components. Parametersmay also include the presence of secreted products such as lymphokines,including IL-2, IL-4, IL-6, growth factors, etc. (Leukocyte Typing VI,T. Kishimoto et al., eds., Garland Publishing, London, England, 1997);Chemokines in Disease: Biology and Clinical Research (ContemporaryImmunology), Hebert, Ed., Humana Press, 1999.

For activated T cells these parameters may include IL-1R, IL-2R, IL4R,IL-12Rβ, CD45RO, CD49E, tissue selective adhesion molecules, homingreceptors, chemokine receptors, CD26, CD27, CD30 and other activationantigens. Additional parameters that are modulated during activationinclude MHC class II; functional activation of integrins due toclustering and/or conformational changes; T cell proliferation andcytokine production, including chemokine production. Of particularimportance is the regulation of patterns of cytokine production, thebest-characterized example being the production of IL-4 by Th2 cells,and interferon-(by Th1 T cells. The ability to shift cytokine productionpatterns in vivo is a powerful means of modulating pathologic immuneresponses, for example in models of EAE, diabetes, inflammatory boweldisease, etc. Thus, the expression of secreted cytokines may be apreferred class of parameters, detectable, for example, by ELISAanalysis of the supernatants, etc.

Candidate Agents

Candidate agents of interest are biologically active agents thatencompass numerous chemical classes, primarily organic molecules, whichmay include organometallic molecules, inorganic molecules, geneticsequences, etc. An important aspect of the invention is to evaluatecandidate drugs, select therapeutic antibodies and protein-basedtherapeutics, with preferred biological response functions. Candidateagents comprise functional groups necessary for structural interactionwith proteins, particularly hydrogen bonding, and typically include atleast an amine, carbonyl, hydroxyl or carboxyl group, frequently atleast two of the functional chemical groups. The candidate agents oftencomprise cyclical carbon or heterocyclic structures and/or aromatic orpolyaromatic structures substituted with one or more of the abovefunctional groups. Candidate agents are also found among biomolecules,including peptides, polynucleotides, saccharides, fatty acids, steroids,purines, pyrimidines, derivatives, structural analogs or combinationsthereof.

Included are pharmacologically active drugs, genetically activemolecules, etc. Compounds of interest include chemotherapeutic agents,anti-inflammatory agents, hormones or hormone antagonists, ion channelmodifiers, and neuroactive agents. Exemplary of pharmaceutical agentssuitable for this invention are those described in, “The PharmacologicalBasis of Therapeutics,” Goodman and Gilman, McGraw-Hill, New York, N.Y.,(1996), Ninth edition, under the sections: Drugs Acting at Synaptic andNeuroeffector Junctional Sites; Drugs Acting on the Central NervousSystem; Autacoids: Drug Therapy of Inflammation; Water, Salts and Ions;Drugs Affecting Renal Function and Electrolyte Metabolism;Cardiovascular Drugs; Drugs Affecting Gastrointestinal Function; DrugsAffecting Uterine Motility; Chemotherapy of Parasitic Infections;Chemotherapy of Microbial Diseases; Chemotherapy of Neoplastic Diseases;Drugs Used for Immunosuppression; Drugs Acting on Blood-Forming organs;Hormones and Hormone Antagonists; Vitamins, Dermatology; and Toxicology,all incorporated herein by reference. Also included are toxins, andbiological and chemical warfare agents, for example see Somani, S. M.(Ed.), “Chemical Warfare Agents,” Academic Press, New York, 1992).

Test compounds include all of the classes of molecules described above,and may further comprise samples of unknown content. Of interest arecomplex mixtures of naturally occurring compounds derived from naturalsources such as plants. While many samples will comprise compounds insolution, solid samples that can be dissolved in a suitable solvent mayalso be assayed. Samples of interest include environmental samples, e.g.ground water, sea water, mining waste, etc.; biological samples, e.g.lysates prepared from crops, tissue samples, etc.; manufacturingsamples, e.g. time course during preparation of pharmaceuticals; as wellas libraries of compounds prepared for analysis; and the like. Samplesof interest include compounds being assessed for potential therapeuticvalue, i.e. drug candidates.

The term samples also includes the fluids described above to whichadditional components have been added, for example components thataffect the ionic strength, pH, total protein concentration, etc. Inaddition, the samples may be treated to achieve at least partialfractionation or concentration. Biological samples may be stored if careis taken to reduce degradation of the compound, e.g. under nitrogen,frozen, or a combination thereof. The volume of sample used issufficient to allow for measurable detection, usually from about 0.1:1to 1 ml of a biological sample is sufficient.

Compounds, including candidate agents, are obtained from a wide varietyof sources including libraries of synthetic or natural compounds. Forexample, numerous means are available for random and directed synthesisof a wide variety of organic compounds, including biomolecules,including expression of randomized oligonucleotides and oligopeptides.Alternatively, libraries of natural compounds in the form of bacterial,fungal, plant and animal extracts are available or readily produced.Additionally, natural or synthetically produced libraries and compoundsare readily modified through conventional chemical, physical andbiochemical means, and may be used to produce combinatorial libraries.Known pharmacological agents may be subjected to directed or randomchemical modifications, such as acylation, alkylation, esterification,amidification, etc. to produce structural analogs.

Genetic Agents

As used herein, the term “genetic agent” refers to polynucleotides andanalogs thereof, which agents are tested in the screening assays of theinvention by addition of the genetic agent to a cell. The introductionof the genetic agent results in an alteration of the total geneticcomposition of the cell. Genetic agents such as DNA can result in anexperimentally introduced change in the genome of a cell, generallythrough the integration of the sequence into a chromosome. Geneticchanges can also be transient, where the exogenous sequence is notintegrated but is maintained as an episomal agents. Genetic agents, suchas antisense oligonucleotides, can also affect the expression ofproteins without changing the cell's genotype, by interfering with thetranscription or translation of mRNA. The effect of a genetic agent isto increase or decrease expression of one or more gene products in thecell.

Introduction of an expression vector encoding a polypeptide can be usedto express the encoded product in cells lacking the sequence, or toover-express the product. Various promoters can be used that areconstitutive or subject to external regulation, where in the lattersituation, one can turn on or off the transcription of a gene. Thesecoding sequences may include full-length cDNA or genomic clones,fragments derived therefrom, or chimeras that combine a naturallyoccurring sequence with functional or structural domains of other codingsequences. Alternatively, the introduced sequence may encode ananti-sense sequence; be an anti-sense oligonucleotide; encode a dominantnegative mutation, or dominant or constitutively active mutations ofnative sequences; altered regulatory sequences, etc.

In addition to sequences derived from the host cell species, othersequences of interest include, for example, genetic sequences ofpathogens, for example coding regions of viral, bacterial and protozoangenes, particularly where the genes affect the function of human orother host cells. Sequences from other species may also be introduced,where there may or may not be a corresponding homologous sequence.

A large number of public resources are available as a source of geneticsequences, e.g. for human, other mammalian, and human pathogensequences. A substantial portion of the human genome is sequenced, andcan be accessed through public databases such as Genbank. Resourcesinclude the uni-gene set, as well as genomic sequences. For example, seeDunham et al. (1999) Nature 402, 489-495; or Deloukas et al. (1998)Science 282, 744-746.

cDNA clones corresponding to many human gene sequences are availablefrom the IMAGE consortium. The international IMAGE Consortiumlaboratories develop and array cDNA clones for worldwide use. The clonesare commercially available, for example from Genome Systems, Inc., St.Louis, Mo. Methods for cloning sequences by PCR based on DNA sequenceinformation are also known in the art.

In one embodiment, the genetic agent is an antisense sequence that actsto reduce expression of the complementary sequence. Antisense nucleicacids are designed to specifically bind to RNA, resulting in theformation of RNA-DNA or RNA-RNA hybrids, with an arrest of DNAreplication, reverse transcription or messenger RNA translation.Antisense molecules inhibit gene expression through various mechanisms,e.g. by reducing the amount of mRNA available for translation, throughactivation of RNAse H, or steric hindrance. Antisense nucleic acidsbased on a selected nucleic acid sequence can interfere with expressionof the corresponding gene. Antisense nucleic acids can be generatedwithin the cell by transcription from antisense constructs that containthe antisense strand as the transcribed strand.

The anti-sense reagent can also be antisense oligonucleotides (ODN),particularly synthetic ODN having chemical modifications from nativenucleic acids, or nucleic acid constructs that express such anti-sensemolecules as RNA. One or a combination of antisense molecules may beadministered, where a combination may comprise multiple differentsequences. Antisense oligonucleotides will generally be at least about7, usually at least about 12, more usually at least about 20 nucleotidesin length, and not more than about 500, usually not more than about 50,more usually not more than about 35 nucleotides in length, where thelength is governed by efficiency of inhibition, specificity, includingabsence of cross-reactivity, and the like.

A specific region or regions of the endogenous sense strand mRNAsequence is chosen to be complemented by the antisense sequence.Selection of a specific sequence for the oligonucleotide may use anempirical method, where several candidate sequences are assayed forinhibition of expression of the target gene. A combination of sequencesmay also be used, where several regions of the mRNA sequence areselected for antisense complementation.

Antisense oligonucleotides can be chemically synthesized by methodsknown in the art. Preferred oligonucleotides are chemically modifiedfrom the native phosphodiester structure, in order to increase theirintracellular stability and binding affinity. A number of suchmodifications have been described in the literature, which alter thechemistry of the backbone, sugars or heterocyclic bases. Among usefulchanges in the backbone chemistry are phosphorothioates;phosphorodithioates, where both of the non-bridging oxygens aresubstituted with sulfur; phosphoroamidites; alkyl phosphotriesters andboranophosphates. Achiral phosphate derivatives include3′-O′-5′-S-phosphorothioate, 3′-S-5′-O-phosphorothioate,3′-CH₂-5′-O-phosphonate and 3′-NH-5′-O-phosphoroamidate. Peptide nucleicacids replace the entire ribose phosphodiester backbone with a peptidelinkage. Sugar modifications are also used to enhance stability andaffinity, e.g. morpholino oligonucleotide analogs. The α-anomer ofdeoxyribose may be used, where the base is inverted with respect to thenatural β-anomer. The 2′-OH of the ribose sugar may be altered to form2′-O-methyl or 2′-O-allyl sugars, which provides resistance todegradation without comprising affinity.

As an alternative method, dominant negative mutations are readilygenerated for corresponding proteins. These may act by several differentmechanisms, including mutations in a substrate-binding domain; mutationsin a catalytic domain; mutations in a protein binding domain (e.g.multimer forming, effector, or activating protein binding domains);mutations in cellular localization domain, etc. See Rodriguez-Frade etal. (1999) P.N.A.S. 96:3628-3633; suggesting that a specific mutation inthe DRY sequence of chemokine receptors can produce a dominant negativeG protein linked receptor; and Mochly-Rosen (1995) Science 268:247.

A mutant polypeptide may interact with wild-type polypeptides (made fromthe other allele) and form a non-functional multimer. For example, ashas been described for dominant negative mutants of the epidermal growthfactor receptor and the chemokine receptor CCR2 (Kashles, 1991, Mol.Cell. Biol, 11:1454; Rodriguez-Frade, 1999, PNAS 96:3628). Mutations ordeletions of catalytic subunits of signaling molecules can also createdominant-negative mutants as, for example, dominant negative mutants ofras and rho family GTPases (Porfiri, 1996, J. Biol. Chem. 271:5871; dePozo, Eur J. Immunol., 1999, 29:3609), protein tyrosine phosphatase 1B(Arregui, 1998, J. Cell Biol. 143:861), and the guanine nucleotideexchange factor CDC25(Mm) (Vanoni, 1999, J. Biol. Chem. 274:36656).Mutations that alter subcellular localization can also create dominantnegative mutants, as for example, a protein kinase B dominant negativemutant described by van Weeren (1998, J. Biol. Chem. 273:13150).Mutations that alter adapter function also create dominant negativemutants, as for example dominant negative mutants of the SH2/SH3adapters Nck and Grb2 (Gupta, 1998, Oncogene, 17:2155) and a deletionmutant of STAT5A (Ilaria, 1999, Blood, 93: 4154).

Preferably, the mutant polypeptide will be overproduced. Point mutationsare made that have such an effect. In addition, fusion of differentpolypeptides of various lengths to the terminus of a protein, ordeletion of specific domains can yield dominant negative mutants.General strategies are available for making dominant negative mutants(see for example, Herskowitz (1987) Nature 329:219, and the referencescited above). Such techniques are used to create loss of functionmutations, which are useful for determining protein function.

Methods that are well known to those skilled in the art can be used toconstruct expression vectors containing coding sequences and appropriatetranscriptional and translational control signals for increasedexpression of an exogenous gene introduced into a cell. These methodsinclude, for example, in vitro recombinant DNA techniques, synthetictechniques, and in vivo genetic recombination. Alternatively, RNAcapable of encoding gene product sequences may be chemically synthesizedusing, for example, synthesizers. See, for example, the techniquesdescribed in “Oligonucleotide Synthesis”, 1984, Gait, M. J. ed., IRLPress, Oxford.

A variety of host-expression vector systems may be utilized to express agenetic coding sequence. Expression constructs may contain promotersderived from the genome of mammalian cells, e.g., metallothioneinpromoter, elongation factor promoter, actin promoter, etc., frommammalian viruses, e.g., the adenovirus late promoter; the vacciniavirus 7.5K promoter, SV40 late promoter, cytomegalovirus, etc.

In mammalian host cells, a number of viral-based expression systems maybe utilized, e.g. retrovirus, lentivirus, adenovirus, herpesvirus, andthe like. In cases where an adenovirus is used as an expression vector,the coding sequence of interest may be ligated to an adenovirustranscription/translation control complex, e.g., the late promoter andtripartite leader sequence. This chimeric gene may then be inserted inthe adenovirus genome by in vitro or in vivo recombination. Insertion ina non-essential region of the viral genome (e.g., region E1 or E3) willresult in a recombinant virus that is viable and capable of expressingthe gene product in infected hosts (see Logan & Shenk, 1984, Proc. Natl.Acad. Sci. USA 81:3655-3659). Specific initiation signals may also berequired for efficient translation of inserted gene product codingsequences. These signals include the ATG initiation codon and adjacentsequences. Standard systems for generating adenoviral vectors forexpression on inserted sequences are available from commercial sources,for example the Adeno-X™ expression system from Clontech (Clontechniques(January 2000) p. 10-12).

In cases where an entire gene, including its own initiation codon andadjacent sequences, is inserted into the appropriate expression vector,no additional translational control signals may be needed. However, incases where only a portion of the gene coding sequence is inserted,exogenous translational control signals, including, perhaps, the ATGinitiation codon, must be provided. Furthermore, the initiation codonmust be in phase with the reading frame of the desired coding sequenceto ensure translation of the entire insert. These exogenoustranslational control signals and initiation codons can be of a varietyof origins, both natural and synthetic. The efficiency of expression maybe enhanced by the inclusion of appropriate transcription enhancerelements, transcription terminators, etc. (see Bittner et al., 1987,Methods in Enzymol. 153:516-544).

In a preferred embodiment, methods are used that achieve a highefficiency of transfection, and therefore circumvent the need for usingselectable markers. These may include adenovirus infection (see, forexample Wrighton, 1996, J. Exp. Med. 183: 1013; Soares, J. Immunol.,1998, 161: 4572; Spiecker, 2000, J. Immunol. 164: 3316; and Weber, 1999,Blood 93: 3685); and lentivirus infection (for example, InternationalPatent Application WO000600; or WO9851810). Adenovirus-mediated genetransduction of endothelial cells has been reported with 100%efficiency. Retroviral vectors also can have a high efficiency ofinfection with endothelial cells, provides virtually 100% report a40-77% efficiency. Other vectors of interest include lentiviral vectors,for examples, see Barry et al. (2000) Hum Gene Ther 11 (2):323-32; andWang et al. (2000) Gene Ther 7(3):196-200.

For the purpose of analysis of the effect of gene over-expressionintroduction of the test gene into a majority of cells (>50%) in aculture is sufficient. This can be achieved using viral vectors,including retroviral vectors (e.g. derived from MoMLV, MSCV, SFFV, MPSV,SNV etc), lentiviral vectors (e.g. derived from HIV-1, HIV-2, SIV, BIV,FIV etc.), adeno-associated virus (AAV) vectors, adenoviral vectors(e.g. derived from Ad5 virus), SV40-based vectors, Herpes Simplex Virus(HSV)-based vectors etc. A preferred vector construct will coordinatelyexpress a test gene and a marker gene such that expression of the markergene can be used as an indicator for the expression of the test gene, aswell as for analysis of gene transfer efficiency. This can be achievedby linking the test and a marker gene with an internal ribosomal entrysite (IRES) sequence and expressing both genes from a singlebi-cistronic mRNA. IRES sequence could be from a virus (e.g. EMCV, FMDVetc) or a cellular gene (e.g. eIF4G, BiP, Kv1.4 etc). The examples ofmarker genes include drug resistance genes (neo, dhfr, hprt, gpt, bleo,puro etc) enzymes (β-galactosidase, alkaline phosphatase etc)fluorescent genes (e.g. GFP, RFP, BFP, YFP) or surface markers (e.g.CD24, NGFr, Lyt-2 etc). A preferred marker gene is biologically inactiveand can be detected by standard immunological methods. Alternatively, an“epitope tag” could be added to the test gene for detection of proteinexpression. Examples of such “epitope tags” are c-myc and FLAG(Stratagene). A preferred viral vector will have minimal or nobiological effect on the biomap apart from the genetic agent beingtested. An example of such viral vectors are retroviral vectors derivedfrom the MoMLV or related retroviruses, as listed above. By gating onthe population of genetically modified cells, the unmodified cells inthe culture can be excluded from analysis, or can be compared directlywith the genetically modified cells in the same assay combination. Forexample, see Bowman et al. (1998) J. Biol. Chem. 273:28040-28048.

Screening Methods

Agents are screened for biological activity by adding the agent to atleast one and usually a plurality of assay combinations to form a panelof assay combinations, usually in conjunction with assay combinationslacking the agent. The change in parameter readout in response to theagent is measured, desirably normalized, and the resulting biomap maythen be evaluated by comparison to reference biomaps. The referencebiomaps may include basal readouts in the presence and absence of thefactors, biomaps obtained with other agents, which may or may notinclude known inhibitors of known pathways, etc. Agents of interest foranalysis include any biologically active molecule with the capability ofmodulating, directly or indirectly, the phenotype of interest of a cellof interest.

The initial screening, particularly a high-throughput screening, mayutilize a panel comprising a single assay combination, while secondaryand higher screenings will generally utilize several assay combinationsin a panel.

The agents are conveniently added in solution, or readily soluble form,to the medium of cells in culture. The agents may be added in aflow-through system, as a stream, intermittent or continuous, oralternatively, adding a bolus of the compound, singly or incrementally,to an otherwise static solution. In a flow-through system, two fluidsare used, where one is a physiologically neutral solution, and the otheris the same solution with the test compound added. The first fluid ispassed over the cells, followed by the second. In a single solutionmethod, a bolus of the test compound is added to the volume of mediumsurrounding the cells. The overall concentrations of the components ofthe culture medium should not change significantly with the addition ofthe bolus, or between the two solutions in a flow through method.

Preferred agent formulations do not include additional components, suchas preservatives, that may have a significant effect on the overallformulation. Thus preferred formulations consist essentially of abiologically active compound and a physiologically acceptable carrier,e.g. water, ethanol, DMSO, etc. However, if a compound is liquid withouta solvent, the formulation may consist essentially of the compounditself.

A plurality of assays may be run in parallel with different agentconcentrations to obtain a differential response to the variousconcentrations. As known in the art, determining the effectiveconcentration of an agent typically uses a range of concentrationsresulting from 1:10, or other log scale, dilutions. The concentrationsmay be further refined with a second series of dilutions, if necessary.Typically, one of these concentrations serves as a negative control,i.e. at zero concentration or below the level of detection of the agentor at or below the concentration of agent that does not give adetectable change in the phenotype.

For identifying the mechanism of action and determining the cellulartarget, a test agent is evaluated in secondary or “biosite identifier”assay combinations. Secondary or “biosite identifier” assay combinationsmay be related to the primary assay combination, but contain specificand targeted alterations. These alterations include addition or deletionof specific assay components, genetic alterations, or inclusion ofspecific compounds or interventions. The mechanism of action of the testagent is accomplished when identical readout response patterns areobtained from assay combinations containing the test agent and assaycombinations generated from known specific alterations of the assaycombination. Alternative pathway activators include compounds, agents orinterventions that stimulate the target pathway through specificcomponents along the target pathway and can bypass upstream regulatorycontrols. The test agent is evaluated in these assay combinations andthe pathway target step is identified as including the most upstreampathway component activator that is sensitive to test agent.

Various methods can be utilized for quantifying the presence of theselected markers. For measuring the amount of a molecule that ispresent, a convenient method is to label a molecule with a detectablemoiety, which may be fluorescent, luminescent, radioactive,enzymatically active, etc., particularly a molecule specific for bindingto the parameter with high affinity. Fluorescent moieties are readilyavailable for labeling virtually any biomolecule, structure, or celltype. Immunofluorescent moieties can be directed to bind not only tospecific proteins but also specific conformations, cleavage products, orsite modifications like phosphorylation. Individual peptides andproteins can be engineered to autofluoresce, e.g. by expressing them asgreen fluorescent protein chimeras inside cells (for a review see Joneset al. (1999) Trends Biotechnol. 17(12):477-81). Thus, antibodies can begenetically modified to provide a fluorescent dye as part of theirstructure

The use of high affinity antibody binding and/or structural linkageduring labeling provides dramatically reduced nonspecific backgrounds,leading to clean signals that are easily detected. Such extremely highlevels of specificity enable the simultaneous use of several differentfluorescent labels, where each preferably emits at a unique color.Fluorescence technologies have matured to the point where an abundanceof useful dyes are now commercially available. These are available frommany sources, including Sigma Chemical Company (St. Louis Mo.) andMolecular Probes (Handbook of Fluorescent Probes and Research Chemicals,Seventh Edition, Molecular Probes, Eugene Oreg.). Other fluorescentsensors have been designed to report on biological activities orenvironmental changes, e.g. pH, calcium concentration, electricalpotential, proximity to other probes, etc. Methods of interest includecalcium flux, nucleotide incorporation, quantitative PAGE (proteomics),etc.

Highly luminescent semiconductor quantum dots (zinc sulfide-cappedcadmium selenide) have been covalently coupled to biomolecules for usein ultrasensitive biological detection (Stupp et al. (1997) Science277(5330):1242-8; Chan et al. (1998) Science 281(5385):2016-8). Comparedwith conventional fluorophores, quantum dot nanocrystals have a narrow,tunable, symmetric emission spectrum and are photochemically stable(Bonadeo et al. (1998) Science 282(5393):1473-6). The advantage ofquantum dots is the potential for exponentially large numbers ofindependent readouts from a single source or sample.

Multiple fluorescent labels can be used on the same sample andindividually detected quantitatively, permitting measurement of multiplecellular responses simultaneously. Many quantitative techniques havebeen developed to harness the unique properties of fluorescenceincluding: direct fluorescence measurements, fluorescence resonanceenergy transfer (FRET), fluorescence polarization or anisotropy (FP),time resolved fluorescence (TRF), fluorescence lifetime measurements(FLM), fluorescence correlation spectroscopy (FCS), and fluorescencephotobleaching recovery (FPR) (Handbook of Fluorescent Probes andResearch Chemicals, Seventh Edition, Molecular Probes, Eugene Oreg.).

Depending upon the label chosen, parameters may be measured using otherthan fluorescent labels, using such immunoassay techniques asradioimmunoassay (RIA) or enzyme linked immunosorbance assay (ELISA),homogeneous enzyme immunoassays, and related non-enzymatic techniques.These techniques utilize specific antibodies as reporter molecules,which are particularly useful due to their high degree of specificityfor attaching to a single molecular target. U.S. Pat. No. 4,568,649describes ligand detection systems, which employ scintillation counting.These techniques are particularly useful for protein or modified proteinparameters or epitopes, or carbohydrate determinants. Cell readouts forproteins and other cell determinants can be obtained using fluorescentor otherwise tagged reporter molecules. Cell based ELISA or relatednon-enzymatic or fluorescence-based methods enable measurement of cellsurface parameters and secreted parameters. Capture ELISA and relatednon-enzymatic methods usually employ two specific antibodies or reportermolecules and are useful for measuring parameters in solution. Flowcytometry methods are useful for measuring cell surface andintracellular parameters, as well as shape change and granularity andfor analyses of beads used as antibody- or probe-linked reagents.Readouts from such assays may be the mean fluorescence associated withindividual fluorescent antibody-detected cell surface molecules orcytokines, or the average fluorescence intensity, the medianfluorescence intensity, the variance in fluorescence intensity, or somerelationship among these.

As an example, Luminex beads or other fluorescent beads, or beadsvarying in light scattering parameters can be conjugated to antibodiesto cytokines or other parameters, or conjugated to protein receptors forparameters. The conjugated beads are added to the cells, cell lysate, orto the removed supernatant, allowing bead binding to target parameters.Also, fluorescent antibody to a distinct epitope of the target parameteris used to measure the level of target parameter bound. The fluorescenceand light scatter characteristics of the beads constitute an identifierof the target parameter, and fluorescence derived from added antibody tothe target parameter is an indication of the quantity of targetparameter bound, and hence a readout of the individual parameter.

Flow cytometry may be used to quantitate parameters such as the presenceof cell surface proteins or conformational or posttranslationalmodification thereof; intracellular or secreted protein, wherepermeabilization allows antibody (or probe) access, and the like.Brefeldin A is commonly utilized to prevent secretion of intracellularsubstances. Flow cytometry methods are known in the art, and describedin the following: Flow Cytometry and Cell Storing (Springer Lab Manual),Radbruch, Ed., Springer Verlag, 2000; Ormerod, Flow Cytometry, SpringerVerlag, 1999; Flow Cytometry Protocols (Methods in Molecular Biology, No91), Jaroszeski and Heller, Eds., Humana Press, 1998; Current Protocolsin Cytometry, Robinson et al., eds, John Wiley & Sons, New York, N.Y.,2000. The readouts of selected parameters are capable of being readsimultaneously, or in sequence during a single analysis, as for examplethrough the use of fluorescent antibodies to cell surface molecules. Asan example, these can be tagged with different fluorochromes,fluorescent bead, tags, e.g. quantum dots, etc., allowing analysis of upto 4 or more fluorescent colors simultaneously by flow cytometry.Plug-flow flow cytometry that has the potential to automate the deliveryof small samples from unpressurized sources at rates compatible withmany screening and assay applications, may allow higher throughput,compatible with high throughput screening, Edwards et al. (1999)Cytometry 37:156-9.

Both single cell multiparameter and multicell multiparameter multiplexassays, where input cell types are identified and parameters are read byquantitative imaging and fluorescence and confocal microscopy are usedin the art, see Confocal Microscopy Methods and Protocols (Methods inMolecular Biology Vol. 122.) Paddock, Ed., Humana Press, 1998. Thesemethods are described in U.S. Pat. No. 5,989,833 issued Nov. 23, 1999.

The quantitation of nucleic acids, especially messenger RNAs, is also ofinterest as a parameter. These can be measured by hybridizationtechniques that depend on the sequence of nucleic acid nucleotides.Techniques include polymerase chain reaction methods as well as genearray techniques. See Current Protocols in Molecular Biology, Ausubel etal., eds, John Wiley & Sons, New York, N.Y., 2000; Freeman et al. (1999)Biotechniques 26(1):112-225; Kawamoto et al. (1999) Genome Res9(12):1305-12; and Chen et al. (1998) Genomics 51(3):313-24, forexamples.

Identifiers of individual cells, for example different cell types orcell type variants, may be fluorescent, as for example labeling ofdifferent unit cell types with different levels of a fluorescentcompound, and the like. If two cell types are to be mixed, one may belabeled and the other not. If three or more are to be included, each maybe labeled to different levels of fluorescence by incubation withdifferent concentrations of a labeling compound, or for different times.As identifiers of large numbers of cells, a matrix of fluorescencelabeling intensities of two or more different fluorescent colors may beused, such that the number of distinct unit cell types that areidentified is a number of fluorescent levels of one color, e.g.,carboxyfluorescein succinimidyl ester (CFSE), times the number offluorescence levels employed of the second color, e.g.tetramethylrhodamine isothiocyanate (TRITC), or the like, times thenumber of levels of a third color, etc. Alternatively, intrinsic lightscattering properties of the different cell types, or characteristics ofthe biomaps of the test parameters included in the analysis, can be usedin addition to or in place of fluorescent labels as unit cell typeidentifiers.

Data Analysis

The comparison of a biomap obtained from a test compound, and areference biomap(s) is accomplished by the use of suitable deductionprotocols, Al systems, statistical comparisons, etc. Preferably, thebiomap is compared with a database of reference biomaps. Similarity toreference biomaps induced by assay combinations involving known pathwaystimuli or inhibitors can provide an initial indication of the cellularpathways targeted or altered by the test stimulus or agent.

A database of reference biomaps can be compiled. These databases mayinclude reference biomaps from panels that include known agents orcombinations of agents that target specific pathways, as well asreferences from the analysis of cells treated under environmentalconditions in which single or multiple environmental conditions orparameters are removed or specifically altered. Reference biomaps mayalso be generated from panels containing cells with genetic constructsthat selectively target or modulate specific cellular pathways. In thisway, a database is developed that can reveal the contributions ofindividual pathways to a complex response.

The effectiveness of pattern search algorithms in classifying biomapscan involve the optimization of the number of parameters and assaycombinations. The disclosed techniques for selection of parametersprovide for computational requirements resulting in physiologicallyrelevant outputs. Moreover, these techniques for pre-filtering data sets(or potential data sets) using cell activity and disease-relevantbiological information improve the likelihood that the outputs returnedfrom database searches will be relevant to predicting agent mechanismsand in vivo agent effects.

For the development of an expert system for selection and classificationof biologically active drug compounds or other interventions, thefollowing procedures are employed. For every reference and test pattern,typically a data matrix is generated, where each point of the datamatrix corresponds to a readout from a parameter, where data for eachparameter may come from replicate determinations, e.g. multipleindividual cells of the same type. As previously described, a data pointmay be quantitative, semi-quantitative, or qualitative, depending on thenature of the parameter.

The readout may be a mean, average, median or the variance or otherstatistically or mathematically derived value associated with themeasurement. The parameter readout information may be further refined bydirect comparison with the corresponding reference readout. The absolutevalues obtained for each parameter under identical conditions willdisplay a variability that is inherent in live biological systems andalso reflects individual cellular variability as well as the variabilityinherent between individuals.

Classification rules are constructed from sets of training data (i.e.data matrices) obtained from multiple repeated experiments.Classification rules are selected as correctly identifying repeatedreference patterns and successfully distinguishing distinct referencepatterns. Classification rule-learning algorithms may include decisiontree methods, statistical methods, naive Bayesian algorithms, and thelike.

A knowledge database will be of sufficient complexity to permit noveltest biomaps to be effectively identified and classified. Severalapproaches for generating a sufficiently encompassing set ofclassification patterns, and sufficiently powerfulmathematical/statistical methods for discriminating between them canaccomplish this.

A database can be compiled by preparing biomaps using differentcombinations of a plurality of biologically active factors, inconjunction with biomaps involving the use of known agents having knowneffects and/or the use of genetically modified cells, where the geneticmodification affects one or more of the pathways affected by one or moreof the factors used to create the phenotype. For example, if the cultureconditions selected to produce a specific in vitro reference patterncontain four biologically active agents, in addition to those present inthe basal conditions of the normal or basal environment, a biomap wouldbe generated from a panel of cells treated under all possiblecombinations of the 4 agents (15 assay conditions), typically usingconstant concentrations in each of the combinations. The extent of thedatabase associated with assay combinations to screen candidates forspecific phenotypes, e.g. indications, will vary with the nature of thephenotype, the amount of information desired, the complexity of thesystem, and the like.

The data from cells treated with specific drugs known to interact withparticular targets or pathways provide a more detailed set ofclassification readouts. Data generated from cells that are geneticallymodified using over-expression techniques and anti-sense techniques,permit testing the influence of individual genes on the phenotype.

As indicated, agents may be analyzed in the absence of any factors orwith a limited number of factors. The assay is performed as previouslydescribed and the values of the parameters can be compared to the biomapreflecting the values for the parameters of the physiologic state ofinterest, the values of the parameters for the response to one or morefactors, and the basal response. In this way, the effect of the agentunder physiological conditions can be evaluated. Similarly, one may havedatasets compiled from combinations of agents to determine their effectwhen combined on cell physiology. Again, with a comparison of the valuesobtained for the parameters with the values obtained from the parameterswith assay combinations employing factors, one can evaluate the effectof the agent combination on various cells in vivo.

A preferred knowledge database contains reference biomaps comprisingdata from optimized panels of cells, environments and parameters. Forcomplex environments, data reflecting small variations in theenvironment may also be included in the knowledge database, e.g.environments where one or more factors or cell types of interest areexcluded or included or quantitatively altered in, for example,concentration or time of exposure, etc.

Pathway Discrimination

Biomaps are useful for pathway discrimination where the biomapsassociated with agents that have a common target and mode of action arereproducibly and robustly similar, where biomaps are associated withagents that stimulate or inhibit different pathways of interestreproducibly, and with biomaps that discriminate at least two,preferably three, and more preferably four or more different pathways ina common set of assay combinations.

Providing an agent to an assay panel results in a biomap that reflectsthe cellular response to that agent, produced by the stimulus acting ona target, or biosite, and through a signaling pathway, producing achange in the phenotype of the cell. A pathway may be defined for thepurposes of the invention as a set of interacting cellular events thatproduces or contributes to a specific phenotype. Pathways are mediatedby sets of interacting molecules of the cell. Variables that act on thesame cellular pathway result in similar biomaps. Similarly, variablesthat act on different cellular pathways result in different biomaps.Variables that act on multiple pathways can stimulate pathwayinteractions and thus also yield distinctive biomaps. It is notnecessary for the purposes of the invention that the cellular pathway isknown.

Comparison of a biomap produced by the action of an agent in a panel, tobiomaps in the database, will indicate whether the variable yields acellular state similar to those generated by other conditions, and thusmay indicate a mechanism of action in the cell, and/or may indicatespecific relevance of the biological activity to a particular disease orother state.

Importantly, compounds that alter therapeutically relevant parametersare of potential interest as drugs. For example, compounds that inhibitcytokine up-regulation of inflammatory cytokines or of molecules(adhesion molecules, chemokines, etc.) involved in leukocyte traffickingto inflamed tissues may have therapeutic value in inflammatory diseases.Compounds that inhibit oncogenic proteins, transcription factorsinvolved with pathways essential to neoplastic proliferation, cyclins,kinases, etc., indicate initial interest as drugs for the treatment ofcancer. Compounds that enhance pathways associated with cholesterolmetabolism and transport may have therapeutic value in cardiovasculardiseases.

Optimization Techniques

Optimized assay combinations can be developed by repeating the procedureof testing parameter readouts in response to stimuli until the selecteddisease-relevant environment is sufficiently differentiated from thenormal or another selected condition and an optimized parameter set isselected.

Optimization of an initial assay combination includes the identificationof optimal concentrations of added biologically active agents, thetiming of their addition, addition or deletion of factors, and selectionof an optimal time course. The time course will depend upon whether oneis interested in the effect of an agent prior to the addition or at thetime of the addition of the factors influencing the parameters or afterthe physiological condition has been established, as well as havingcells that do and do not present the physiologic condition. The factorsmay have been present from about 0 to 72 h or longer prior to theaddition of the agent, usually from about 0 to 48 h, and frequently fromabout 0 to 24 h. Where the cells may be at various stages of thephysiologic condition, e.g. unchanged, intermediate stage and finalstage the factors will usually have been present from about 2 to 48 h orlonger, more usually from about 6 to 24 h. Optimization also includesmodification of the basal medium (e.g. the addition or removal ofparticular growth factors, extracellular matrix components etc.) toreflect differences between physiologic states of interest.

For the most part, the concentration of the factors for providing thephysiologic condition will be known and frequently the response will notbe sensitive to small changes in the concentration. Where theconcentration has not been reported, one can determine a usefulconcentration by determining the concentration that provides saturation.This can be achieved using cells and titrating the number of receptorswith a labeled factor, e.g. fluorescent labeled factor. Once thesaturation level is known, one may cut back to about 25 to 75% of thesaturation value and determine the response by analyzing for theparameters of interest and the effect of the reduced concentration ascompared to the response at saturation. Alternatively, take the factorto a plateau of a dependent functional response, add more or less todefine levels maximal to response measures.

Active compounds alter the cellular responses and readout patterns whenincluded in a selected assay combination. Such alteration may includereturning the levels of one or more parameters to their levels in thebasal condition, or otherwise altering the cellular responses,particularly when such alterations reflect changes towards a desirablecellular state (e.g. converting Th1-like to Th2-like response, or viceversa).

Optimal assay combinations yield information about multiple differentpathways of interest in regulation of inflammatory processes. Conditionsbased on initial combinations are developed to better reflect thephysiologic or disease-relevant environment. Optimized assaycombinations are developed by repeating the procedure to produce abiomap, evaluating additional combinations of biologically active agentsand/or different parameters, until the biomap produced under theselected disease-relevant environment is sufficiently differentiatedfrom the biomap of the normal or another selected condition and anoptimized parameter set is selected.

Cell Families Endothelial Cells

As exemplary of the subject situation, primary endothelial cells areemployed in one embodiment of the invention, as these cells respond to alarge variety of cellular stimuli. Endothelial cells are highlysensitive to their environment, and they contain a large number ofsignaling pathways. This provides an opportunity to evaluate the effectof compounds on many pathways and/or pathway interactions. Endothelialcells participate in many disease processes. In inflammation, theycontrol the migration and localization of effector leukocytes andlymphocytes; in cancer, they control the nutrition of tumors anddissemination of metastases; and their dysregulation is centrallyimportant to cardiovascular disease.

The present invention is useful for identifying regulators ofinflammation using human endothelial cells as an indicator cell type.Endothelial cells are found in inflammatory tissues; they are highlyresponsive to environmental stimuli; and they are a cell type for whichprimary cells can be readily isolated and cultured such that they retainresponsiveness to many of the biologically active factors important toinflammatory and other processes. Vascular endothelial cells are apreferred cell type because they participate in the inflammatory diseaseprocess by regulating the type of leukocytes that are recruited to thetarget tissue. The specificity of recruitment is determined by thecombinatorial expression of adhesion molecules and chemokines. A set ofculture systems or assay combinations that mimic the response of theendothelial cells to different types of inflammatory processes have beendeveloped in vitro using the methods of the invention.

A number of factors are known to be associated with endothelial cells,such as EGF, FGF, VEGF, insulin, etc., cytokines, such as theinterleukins, including IL-1 IL-3, IL-4, IL-8 and IL-13; interferons,including IFN-α, IFN-β, IFN-γ; chemokines; TNF-α, TGFβ, proangiogenicand anti-angiogenic factors, etc. (See Current Protocols in Immunology,supra).

Endothelial cells in inflammatory tissues from chronic inflammatorydisease patients differ from endothelial cells in normal tissues byincreased expression parameters including ICAM-1, E-selectin, IL-8 andHLA-DR [Nakamura S, Lab Invest 1993, 69:77-85; Geboes K,Gastroenterology 1992, 103:439-47; Mazzucchelli L, J Pathol 1996,178:201-6]. In addition, each of these parameters has been demonstratedto function in the inflammatory disease process. ICAM-1 and E-selectinare cell adhesion molecules that contribute to the localization andactivity of inflammatory cells including T cells, monocytes, andneutrophils. IL-8 is a neutrophil chemoattractant and HLA-DRparticipates in the activity of pathologic T cells. Other cell surfaceor secreted parameters include parameters that are known to be regulatedby factors, such as VCAM-1, which is induced on endothelial cells byTNF-α or IFN-γ; IL-10 and MIG which are induced on endothelial cells byIFN-γ; or GRO-α or ENA-78 which are induced on endothelial cells by IL-1and/or TNF-α [Goebeler M, J Invest Dermatol 1997, 108:445-51; Piali LEur J. Immunol. 1998, 28:961-72].

For assay combinations representative of chronic inflammatory diseases,the cytokine IL-1 is often found in combination with TNF-α and IFN-γ insuch diseases, for example, in Crohn's disease (Autschbach, 1995,Virchows Arch. 426:51-60). For this inflammation model of endothelialcells, an inhibitor of TNF-∀, such as a neutralizing antibody againstTNF-∀, provides an example of an active compound. Adding anti-TNF-α tothe assay combination was shown in reduced expression levels of ICAM-1;VCAM-1; and E-selectin; and increased expression levels of CD31.

Assay combinations that include genetically modified cells are also apreferred source of reference patterns. For example, TNF-α signaling inHUVEC involves the NFκB signaling pathway (Collins, 1995, Faseb J,9:899). Blockade of this pathway can be accomplished by overexpressionof IκB-α, for example, through adenoviral gene transfer (Weber, 1999,Blood 93:3685). HUVEC overexpressing IκB-α express reduced levels ofICAM-1 or E-selectin in response to TNF-α. However, because othercytokines, such as IL-1, can also signal through NFκB, readout patternsdue to TNF-α inhibition can be distinguished from readout patterns thatreflect NFκB inhibition.

Leukocytes

By a similar iterative process as that described above, appropriateassay combinations for endothelial cells representing otherinflammatory, disease, or physiologic states are established. Theseconditions include: psoriasis, rheumatoid arthritis, or chronic Th2disease environments such as asthma, allergy or ulcerative colitis. Achronic Th2 assay combination can be defined by the culture of HUVECwith TNF-α and/or IL-1 and IL-4 for 24 hours. Inflammation in chronicTh2 environments, such as asthma, is characterized by the presence ofTNF-α, IL-1 and IL-4, but not IFN-γ [Robinson, 1993, J. Allergy Clin.Immunol. 92:313]. HUVEC cultured for 24 hours with TNF-α and IL-4express high levels of VCAM and MCP-1, similar to the in vivo situation[Ohkawarea, 1995, Am J. Resp. Cell Mol. Biol. 12:4; Rozyk, 1997,Immunol. Lett. 58:47].

Lymphokine-producing activated lymphocytes (CD45RO+, CD44hi, etc.) are ahallmark of inflammatory diseases including psoriasis, rheumatoidarthritis, Crohn's disease, ulcerative colitis, asthma, etc. Dependingon the disease environment and tissue site, activated lymphocytes candiffer in their expression and function of adhesion molecules and otherreceptors, as well as in their production of various cytokines and otherfactors. The ability to selectively block lymphocyte activationassociated with the inflammatory disease without inhibiting orsuppressing lymphocyte activation associated with the ability to fightinfection and neoplasia is a goal of inflammatory drug therapy.

Specific homing and adhesion receptors, as well as chemokine receptors,expressed by lymphocytes differentiating into effector and memory cellstarget the involved regulatory and cytotoxic T cell populations, as wellas B cells responsible for humoral immunity. Upregulation and modulationof homing receptor expression patterns is observed when lymphocytes areactivated in defined microenvironments comprising specific cytokines;and in some environments multiple homing receptors (e.g., α₄β₇, thecutaneous lymphocyte antigen (“CLA”), inflammatory chemokine receptorsuch as CCR5 and CXCR3 and bonzo, etc.) are induced. Multiplex analysisof each of these homing receptor parameters, which may also be performedin conjunction with other known or discovered parameters in reflectingthe cellular state of activation, can be used to select immunomodulatorycompounds capable of shifting patterns of homing receptor expression ina common microenvironment. Such modulators of lymphocyte targeting canbe powerful immunosuppressives for localized immune pathologies, as ininflammatory bowel diseases, psoriasis, multiple sclerosis, arthritis,and the like; modulating patterns of lymphocyte homing/targetingmolecules they would modulate in vivo immune responses therapeuticallywithout the side effects associated with generalized immunosuppression.

The present invention can be applied to screening for drugs that blockselective leukocyte activation pathways. Cells can be normal lymphocytesor lymphocyte subsets isolated from human blood or tissues according tostandard methods (Current protocols in Immunology), or cell linesselected for their ability to respond in a similar fashion as do normalcells, or other cells.

The assay conditions for these cells include (1) known activationconditions ((combinations of anti-CD3+IL-2+/−IL-4+/−IFN-(+/−IL-12+/−anti-IL-4 or anti-IFN -γ). Such conditions are given in: TCell Protocols: Development and Activation (Methods in MolecularBiology, 134), Kearse, Ed., Humana Press, 2000.); (2) culture conditionsthat represent in vivo disease environments; or (3) conditions thatemphasize or discriminate known signaling pathways or specific signalingpathways implicated in disease states. Assay combinations and referencebiomaps are identified for a variety of diseases, including psoriasis,arthritis, Crohn's disease, ulcerative colitis, asthma, etc. by theiterative process as described in Example 1, of defining environmentalconditions and initial parameter sets from in vivo data, testing assaycombinations in vitro, comparing the in vitro and in vivo biomaps,optimizing the assay combination and selection of an optimal parameterset.

The disease environment in psoriasis includes IL-12, IFN-γ and TNF-α(Yawalker, 1998, J. Invest. Dermatol. 111:1053; Austin, 1999, J. Invest.Dermatol. 113:752), therefore an assay combination for psoriasis willinclude one or more, usually at least two, and frequently all of thesefactors. Inflammatory T cells in psoriasis express high levels of theCLA antigen, a carbohydrate antigen related to Sialyl Lewis x (Berg,1991, J. Exp. Med. 174:1461; Picker, 1990, Am. J. Pathol. 136:1053).Therefore a parameter set for psoriasis will contain the CLA antigen.

The disease environment in Crohn's disease includes IL-1, TNF -α, IL-6,IL-8, IL-12, IL-18, and IFN -γ (Daig, 1996; Woywodt, 1994; Kakazu, 1999;Pizarro, 1999; Monteleone, 1999), therefore an assay combination forCrohn's disease will include one or more of these factors, generallyincluding at least two of the IL factors, by themselves or incombination with at least one of IFN-γ and TNF-α. T cells ininflammatory bowel disease express high levels of the E7 integrin(Elewaut, 1998, Scand J. Gastroenterol, 33:743), therefore the parameterset for inflammatory bowel diseases preferentially contains E7.

The disease environment in rheumatoid arthritis includes TNF-α, IL-1,IL-6, IL-10, IL-15, MIP1, MCP-1, and TGF (Robinson, 1995, Clin. Exp.Immunol. 101:398; Thurkow, 1997, J. Pathol. 181:444; Suzuki, 1999, Int.Immunol, 11:553), therefore an assay combination for arthritis willinclude one or more of these factors, generally including at least twoof the IL factors and at least one of MIP1 and MCP-1. T cells inrheumatoid arthritis synovial fluid express CCR5 and CXCR3 (Suzuki,1999; Qin, 1998, J. Clin. Invest. 101:746; Loetscher, 1998, Nature391:344), therefore the parameter set for rheumatoid arthritispreferentially contains CCR5 and CXCR3.

The disease environment in asthma includes IL-_(.)1α, IL-4, IL-5, IL-6and GM-CSF (Miadonna, 1997; Walker, 1994), therefore, an assaycombination for asthma will contain one or more of these factors,generally including at least two of the IL factors and GM-CSF.

Once the optimal environmental conditions representing the targetdisease are determined, cells are treated with candidate drugs in thoseenvironments and the selected parameters are measured. Comparing thebiomaps obtained in the presence of drugs with reference biomaps enablesthe identification of drugs that inhibit lymphocyte responses to complexenvironments, and enables them to be differentiated from drugs that acton selective pathway components. The multiplex living response systemsallows simultaneous analysis of multiple activation-associatedparameters, and Th1 versus Th2 phenotypes; as well as comparison of theeffects candidate drugs have on T cell activation programs with theireffects on properties of other cells in the living response systemutilized. As an example, in its simplest embodiment, normal human Tcells or blood lymphocytes are incubated in an activating and/ordifferentiating environment, contacted with an agent, and the readoutoutput patterns compared with reference patterns obtained under controlconditions (without the compound) and in the presence of prototypicalanti-inflammatory compounds etc.

This is accomplished by developing a database of reference biomapsdeveloped from the analysis of cells treated under environmentalconditions in which single components are removed, or with known drugsthat target specific pathways. Alternatively, reference biomaps aregenerated in the presence of genetic constructs that selectively target,stimulate, inhibit or otherwise modulate specific pathways. In this way,a database of reference biomaps is developed.

One preferential application of the invention is in immune deviation.Certain inflammatory diseases result or are exacerbated by polarizationof an inflammatory response towards Th1 or Th2. For example, conditionsthat promote Th1 responses (e.g. systemic treatment with IFN -γ)exacerbate certain diseases such as multiple sclerosis. By the proceduregiven above, compounds can be screened for their ability to shiftbiomaps from “Th1” to “Th2”, vice versa, or from “Th1” or “Th2” to otherphenotypes.

The invention is also useful for screening compounds for druginteractions. For example, methotrexate is a current therapy forrheumatoid arthritis and inhibits T cell proliferation. Screeningcompounds in the presence of methotrexate can reveal unexpectedtoxicities or beneficial synergies.

The present invention can be applied for the identification of compoundsthat induce lymphocyte activation. For this application, drug compoundsmay be screened for their ability to induce particular referencebiomaps. Such compounds would have clinical utility as immunestimulants, for vaccine protocols and other applications.

The present invention can be applied to the identification of compoundsthat stimulate or inhibit lymphocyte apoptosis. A variety of cultureconditions are known to induce apoptosis in particular cell types. Forexample, radiation; inclusion of FasL in the culture; or other apoptosisinducing agent, can induce apoptosis of FasR (CD95) expressing T cells;TNF-∀ can induce apoptosis under specific conditions; a conformationalchange in ICAM-3, resulting in a change in ligand preference (from LFA-1to a macrophage receptor) is associated with apoptosis in activated Tcells, etc. The ability of a drug or intervention to induce apoptosishas applications for therapy of lymphoma and leukemia as well asautoimmune disease. Defining biomaps associated with apoptosis areuseful for identifying active compounds.

Macrophage

The present invention can be applied to the identification of compoundsthat inhibit or alter macrophage activation. Peripheral blood monocytes,tissue macrophages and related cell lines are a preferred cell type forscreening for pharmacologically active compounds/interventions due totheir ability to discriminate pathophysiological environments.Monocytes/macrophages in different physiological settings have alteredresponses. IL-4 reduces production of IL-10 in LPS stimulated bloodmonocytes but not in synovial monocyte/macrophages (Bonder (1999)Immunol. 96:529; Ju (1999) Int. Rev. Immunol. 18:485). In addition tobeing highly responsive to their environment, monocytes/macrophagesparticipate in a variety of disease processes, including inflammation,fibrosis, and wound healing, through their production of mediators,growth factors, phagocytosis and antigen presentation functions. Assaycombinations, e.g. IL-4 and other IL factors, M-CSF, and GM-CSF are usedin combination with each other or other factors associated with thephysiologic or disease environments of interest and readout parametersets are selected that allow different states to be distinguished.Readout parameters include integrins, adhesion molecules, and the like.Compounds are added to selected assay combinations, parameters aremeasured and the resulting test patterns are compared to referencebiomaps. Reference patterns, held in a knowledge database include thosedeveloped from the analysis of cells treated under environmentalconditions in which single components are removed, or with known drugsthat target specific pathways. Alternatively, reference biomaps can begenerated in the presence of genetic constructs that selectively target,stimulate, inhibit or otherwise modulate specific pathways. In this way,a database of reference biomaps is developed, and compounds are selectedby their ability to produce a desired biomap.

Mast Cell

The present invention can be applied to the identification of compoundsthat inhibit or alter mast cell activation. Such compounds have utilityin the treatment of allergy and asthma, where mast cell products mediatedisease pathology (Galli, 2000, Curr. Opin. Hematol. 7:32). Mast cellsdisplay altered responses depending on their environment. The ability ofmast cells to produce IL-3 and GM-CSF is significantly increased in thepresence of fibronectin or vitronectin (Kruger-Krasagakes, 1999,Immunology, 98:253). Mast cells in allergen-induced late-phase cutaneousreactions in atopic patients express high levels of the high affinityIgE receptor compared with mast cells in control skin (Ying, 1998,Immunology 93:281). Assay combinations including at least one offibronectin and vitronectin are developed that reflect physiologic ordisease environments and readout parameter sets, including at least oneof IL-3, GM-CSF, and IgE-receptor, are selected that allow differentstates to be distinguished. Compounds are added to selected assaycombinations, parameters are measured and the resulting test patternsare compared to reference biomaps. Reference patterns, held in aknowledge database include those developed from the analysis of cellstreated under environmental conditions in which single components areremoved, or with known drugs that target specific pathways.Alternatively, reference biomaps can be generated in the presence ofgenetic constructs that selectively target, stimulate, inhibit orotherwise modulate specific pathways. In this way, a database ofreference biomaps is developed, and compounds are selected by theirability to produce a desired biomap.

Cancer Applications

a. Cytolytic/Cytostatic Compounds

The unique comparisons between panels of cell types holds the potentialto provide therapeutically important information, and allowsubclassification, of drugs and genes that can inhibit neoplastic cellproliferation, alter the immunogenicity, or modulate other criticalfeatures for cancer therapy. A panel of 60 neoplastic cell lines at theNCl has been used to examine the effects of hundreds of anti-cancer andother compounds on neoplastic cell proliferation (Weinstein, 1997,Science 275:343). While the responses of any individual cell linecarried little information about the mechanism of inhibition ofproliferation, the patterns of responses among the 60 cells of the paneldemonstrated a robust ability to distinguish between compounds targetingdifferent mechanisms, and thus to characterize the mechanisms of actionof novel drugs as well, by comparison with reference tumor panelresponse patterns.

The present invention is applied by identifying subsets of the 60 NIHcell lines, and other cell lines that can provide robust discriminatorypower for identifying and subclassifying anti-cancer agents. Theresponses of cell surface proteins and/or secreted products such aschemokines and other cytokines and the like, is determined underenvironmental conditions supportive of the neoplastic proliferativephenotype. Breast cancer environments involve certain growth factors,e.g. angiogenic factors and cytokines, such as IL-10 (Merendino 1999,68, 355). Alterations in the selected parameters by contact of the cellswith anti-cancer agents is used to define reference biomapscharacteristic and diagnostic of individual drugs or mechanisms ofaction. The use of cell surface parameters to identify cytotoxic andcytostatic states allows a panel of cells to be evaluated in parallel.Biomaps are generated from known anti-cancer agents including DNAsynthesis inhibitors, nucleoside analogs, topoisomerase inhibitors,microtubule function inhibitors etc. Such compounds are given inWeinstein, 1997, and The Pharmacologic Basis of Therapeutics. Referencepatterns that distinguish compounds that are cytostatic or cytolyticversus apoptosis-inducing are developed using a panel of primary tumorsand tumor cell lines with and without functioning p53 pathways. Theprocedure of simultaneous multiplex analyses of normal and cancer celllines allows discrimination of agents selective for cancer cells.

The invention is also useful for screening compounds for druginteractions and synergies. Drug interactions are highly important incancer therapy. For example, while steroids control the edema thatoccurs with glioma, they also interfere with chemotherapy efficacy.Cytotoxic drugs are a main treatment for cancer and interference withthe chemotherapy efficacy may offset the anti-tumor effect of anapoptosis inducer. On the other hand, synergy between individual drugswould be highly beneficial, perhaps allowing reduced doses of theindividual drugs and reducing the side effects.

b. Inhibitors of Metastatic Phenotype

The present invention can be applied to the identification of compoundsor interventions that alter metastatic phenotypes of cancer cells.Metastatic cancers have altered adhesive and invasive functions.Metastatic cancers are associated with certain features includingexpression of various oncogenes, such as H-ras, increased levels ofproteolytic enzymes, such as TPA (tissue plasminogen activator),production of osteopontin, and altered adhesion molecule expression andfunction. For example, carcinomas preferentially express α₆β₁ and lessα₂β₁, α₃β₁ and α₅β₁ (Chambers 1993, Crit. Rev. Oncol. 4:95; Dedhar,1995, Cancer Metastasis Rev. 14:165; Tuck, 1999, Oncogene 18:4237).Simultaneous multiplex analyses of normal and cancer cell lines allowsdiscrimination of agents that selectively modulate the metastaticphenotype.

c. Inducers of Differentiative Phenotypes.

There is a general inverse relationship between the degree of cellulardifferentiation and the rate of cell proliferation in tumors. Severalanti-cancer agents stimulate the differentiation and inhibitproliferation of malignant cells, including retinoids, various cytokinesand analogs of vitamin D (Bollag, 1994, J. Cell Biochem. 56:427).All-trans retinoic acid, an agent that induces differentiation, gives ahigh rate of complete clinical remission in the treatment of acutepromyelocytic leukemia (Tallman, 1994, Semin Hematol 31 (Suppl 5):38).Agents that stimulate differentiation are not easily detected usingtraditional in vitro assays of anticancer drug activity.

d. Apoptosis of Tumor Endothelial Cells.

The present invention can be applied to the identification of compoundsthat induce apoptosis of tumor endothelial cells. For this application,environmental conditions that induce a tumor endothelial cell phenotypeon cultured endothelial cells are selected. Typically these environmentsare proangiogenic and contain a variety of growth factors, such as TGFβ,VEGF and basic FGF, as well as other tumor or other cell derivedfactors, where these factors can be used in the assay combination. Tumorendothelium differs from other endothelium by increased expression ofα_(v)β₃. A set of conditions that induce apoptosis of these cells isevaluated and a set of parameters that defines a biomap diagnostic ofapoptosis is identified. Apoptotic conditions are identified as thosethat induce DNA laddering, and other well described features. Theseinclude simple culture conditions that contain one or more factors knownto induce or promote endothelial cell apoptosis in vitro, such asceremide, the combination of TNF-∀ and heat shock or sodium arsenite,TNF-α+IFN-γ, oxysterols; TNF-α in the presence of cyclohexamine, etc.(See Ruegg (1998) Nat. Med. 4:408). Parameters that may be included inthe selected set include a variety of molecules involved in adhesion andproteolysis (since a prominent feature of apoptotic endothelial cells istheir release from the vessel wall), those that can be modulated byindividual factors, such as E-selectin, ICAM-1, VCAM and HLA-DR, andmolecules or determinants known to be modulated with apoptosis such asCD95, ICAM-1, CD44, and carbohydrate determinants (Herbst, 1999, J. CellPhysiol. 181:295; Rapaport, 1999, Glycobiology 9:1337; Hirano (1999)Blood 93:2999; Thomas (1998) J. Immunol. 161:2195; Ma (1998) Eur. J.Hematol. 61:27; Pober (1998) Pathol. Biol. (Paris) 46:159).

Once a reference biomap for endothelial cell apoptosis is identified,compounds are screened for their ability to induce a similar biomap fromtumor, but not normal, endothelial cells. Test patterns are compared toa database of reference biomaps that includes patterns obtained from theanalysis of cells treated under environmental conditions in which singlecomponents are removed, or with known drugs that target specificpathways. Alternatively, reference biomaps are generated in the presenceof genetic constructs that selectively target specific pathways. In thisway, a database of reference biomaps is developed that can reveal thecontributions of individual pathways to a complex response.

Angiogenesis Inhibitors

The present invention can be applied to the identification of compoundsthat inhibit or modulate angiogenesis. Pharmacologic modulation ofangiogenesis has applications to the treatment of cancer, wherevascularization of tumors contributes to cancer growth; for inflammatoryconditions such as arthritis where neovascularization supportsinflammatory cell influx; wound healing; and others. A number ofbiologically active agents are known to induce or promote angiogenesisincluding VEGF, FGF, IL-8, IL-4, various extracellular matrixcomponents, etc., where at least 2, usually at least 3 of these factorsmay be used in an assay combination. Physiologically relevant states invivo are complex, containing combinations of factors and otherconditions. The environment of rheumatoid arthritis, in which angiogenicfactors are present in a proinflammatory environment, can bedistinguished from tumor environments that may be characterized byreduced oxygen and the presence of various growth factors in combinationwith a pro-angiogenic environment. Culture environments for endothelialcells that reflect these disease or physiological environments aredeveloped through an iterative process of (a) identifying factors thatare known to be expressed at the disease site. For example,vascularizing arthritis environments contain basic FGF and VEGF inaddition to TNF-α, IL-1, IL-6, IL-10, IL-15, MIP1β and MCP-1 (Qu, 1995,Lab Invest., 73:339; Koch, J. Immunol. 1994, 152:4149; Robinson, 1995,Clin. Exp. Immunol. 101:398; Thurkow, 1997, J. Pathol. 181:444; Suzuki,1999, Int. Immunol, 11:553). The disease environments of highlyvascularized tumors includes hypoxia, VEGF, fibrinogen and TGF-β(Senger, 1994 Invasion Metastasis, 95:385; Shweiki, 1992, Nature,359:843). The iterative process then (b) identifies a set of parametersthat includes those that are known to be differentially regulated by oneor more of the factors identified in (a), or parameters includingadhesion molecules, receptors, chemokines, etc., that are known to bedifferentially expressed by angiogenic endothelium at the disease sites.These may include the expression of functional forms of adhesionmolecules such as α_(v)β₃, VCAM, proteases, such as matrixmetalloproteinases, or other substances. The process then c) evaluatesthe effects of environments containing combinations of factors on theexpression of parameters on endothelial cells in vitro; and d) selectsconditions (factor composition, time course, concentration, etc.) thatresult in the pattern of expression of parameters that is representativeof the in vivo phenotype. Optimization of the final set of environmentalconditions and parameters is carried out by testing larger panels ofparameters under the different environmental conditions in vitro andselecting those that can discriminate between two or more environments,said environments differing by one or more individual environmentalcomponents. This procedure can be performed in a high throughput manner,and individual selected parameters can be confirmed by evaluating theexpression in vivo under normal and disease tissues. The goal of theabove process is the identification and selection of a minimal set ofparameters, each of which provides a robust readout, and that togetherenable discrimination of each environmental condition.

Once a panel of environments is identified, and an optimal set ofparameters is selected, cells are treated under each condition and adatabase of reference biomaps is developed. These include referencebiomaps from cells treated under environments that include known drugsthat target specific pathways, as well as reference biomaps from theanalysis of cells treated under environmental conditions in which singleor multiple components are removed. For example, for an assaycombination representing endothelial cells in a vascularizing arthritisenvironment, reference biomaps are developed from assay combinations inwhich single components (e.g. VEGF) might be removed. Reference biomapsare also generated from cells containing genetic constructs thatselectively target specific pathways. In this way, a database ofreference biomaps is developed that can reveal the contributions ofindividual pathways to a complex response.

With such a database, the invention provides for preferential selectionof drug compounds that inhibit angiogenic responses in complexenvironments. Such compounds would be identified by their ability toinduce a biomap consistent with inhibition of an angiogenic phenotype inthe presence of a complex environment. Compounds that selectively blockthe response to a single factor or component of the complex environment(e.g. FGF receptor signaling, etc.) would be revealed by a biomapconsistent with the response pattern in the absence of that factor (e.g.FGF, etc.)

The invention is also useful for screening compounds for druginteractions. Drug interactions can be problematic in cancer therapy.For example, while steroids control the edema that occurs with glioma,they also interfere with chemotherapy efficacy. Cytotoxic drugs form thebasis of many cancer therapies, thus interference with chemotherapyefficacy may offset any anti-tumor effects of angiogenesis inhibitors.Most cytotoxic drugs effect both normal and neoplastic cells, althoughat different concentrations, therefore, screening compounds in thepresence of cytotoxic drugs can be performed and reveal unexpectedinterference or beneficial synergies. Interactions between a cytotoxicdrug and any test compound would be detected by the observation ofbiomaps obtained in the presence of both drugs that are inconsistentwith additive effects.

Modulators of Bone Development

Modulation of bone development and remodeling has application for thetherapy of osteoporosis, atherosclerosis, and rheumatoid arthritis, allsituations where undesired bone destruction, bone formation ormorphogenesis occurs. Bone-forming osteoblasts are derived from a commonprecursor in bone marrow that differentiates into osteoblasts oradipocytes depending on the differentiation environment. Factorsassociated with osteoblast development include estrogen, bonemorphogenic proteins and TGF-β. Differentiation of osteoblasts isassociated with the production of alkaline phosphatase, type I collagen,osteopontin and the ability to mineralize calcium. Factors associatedwith adipocyte development include FGF and glucocorticoids.Differentiation of adipocytes is associated with their production ofPPAR(2, lipoprotein lipase and leptin. Optimized culture environmentsare defined for the relevant disease or physiologic states as describedabove and a set of parameters that distinguish adipocyte and osteoblastdifferentiation are selected. For screening compounds for inhibitors ofosteoporosis, test compounds are screened for their ability to promoteosteoblast development in the relevant disease environment. For example,in the case of older women, that would include low estrogen levels; inthe case of autoimmune disease patients on long term glucocorticoidtherapy, the environment may contain dexamethasone, and so on.

Modulation of osteoclast development and function has applications forbone remodeling that occurs in rheumatoid arthritis. Osteoclasts developfrom CD14+ monocytes. Factors that promote osteoclast developmentinclude TRANCE (RANKL or osteoprotegrin ligand), TGFβ and M-CSF.Rheumatoid arthritis environments also contain TNF-α, IL-1, IL-6, IL-10,IL-15, MIP1β and MCP-1 (Robinson, 1995, Clin. Exp. Immunol. 101:398;Thurkow, 1997, J. Pathol. 181:444; Suzuki, 1999, Int. Immunol, 11:553).Optimized culture environments are defined for osteoclasts or precursorCD14+ monocytes in pro-osteoclast development arthritis environment. Aset of parameters is selected that identifies osteoclasts in such anenvironment. Osteoclast function is associated with expression ofcalcitonin, vitronectin receptors, cathepsis k, carbonic anhydrase II,vacuolar (H(+)) ATPase, tartrate-resistant ATPase and osteopontin. Forscreening compounds to identify inhibitors of osteoclast development orfunction, active compounds are identified by their ability to inhibitosteoblast development in the relevant disease environment.

Neurobiology Applications Alzheimer's Disease

A prominent feature of Alzheimer's disease patients is activated glia(astrocytes and microglia) in close proximity to amyloid plaques. Thesecells express increased levels of Class II antigens,alpha-1-antichymotrypsin, IL-1β, S-100β and butyrylcholinesterase. Thedisease environment in Alzheimer's disease contains IL-1, IL-6 and theβ-amyloid peptide 1-42.

Regulators of Hematopoiesis

Mesenchymyl stem cell cultures can be provided with environments leadingto fibroblastic, osteoblastic, or adipocyte differentiation, eachassociated with unique patterns of cell surface and secreted moleculeexpression defining these cellular states. A set of parameters thatidentifies various lineages of hematopoietic cells (e.g. erythroid,myeloid, T versus B, NK, etc.) are selected. Compounds that alter thedifferentiation of selected cell types are selected by their ability toproduce biomaps characteristic of that population.

Kits

For convenience, the systems of the subject invention may be provided inkits. The kits could include the appropriate additives for providing thesimulation, optionally include the cells to be used, which may befrozen, refrigerated or treated in some other manner to maintainviability, reagents for measuring the parameters, and software forpreparing the biomap. The factors will be selected that in conjunctionwith the cells would provide the desired physiological state simulatingthe in vivo situation. The factors could be a mixture in the appropriateproportions or provided individually. For example, IL-1, TNF-α, andIFN-γ would be combined as a powder to be measured for addition to thecell medium and labeled antibodies to parameters, such as ICAM-1, VCAM-1and E-selectin, in conjunction with second capture antibodies or usingantibodies for homogeneous assays, where another reagent is present. Thesoftware will receive the results and create a biomap and can includedata from other assay combinations for comparison. The software can alsonormalize the results with the results from a basal culture and/or thebasal culture including the factors.

EXPERIMENTAL Example 1 Regulators of Endothelial Cell Responses toInflammation

The present invention is useful for identifying regulators ofinflammation using human endothelial cells as an indicator cell type. Aset of assay combinations that reproduces aspects of the response of theendothelial cells to different types of inflammatory processes isdeveloped in vitro.

Primary human umbilical vein endothelial cells (HUVEC) are used. Othercells that may replace HUVEC in the screen include primary microvascularendothelial cells, aortic or arteriolar endothelial cells or endothelialcell lines such as EAhy926 or E6-E7 4-5-2G cells or human telomerasereverse transcriptase-expressing endothelial cells (Simmons, J.Immunol., 148:267, 1992; Rhim, Carcinogenesis 19:673, 1998; Yang, J.Biol. Chem. 274:26141, 1999). 2×10⁴ cells/ml are cultured to confluencein EGM-2 (Clonetics). Other media that may replace EGM-2 include EGM(Clonetics) and Ham's F12K medium supplemented with 0.1 mg/ml heparinand 0.03-0.05 mg/ml endothelial cell growth supplement (ECGS) and 10%FBS, or medium M199 (Life Technologies, Inc.) containing 20% fetalbovine serum and 2 ng/ml basic fibroblast growth factor (Jaffe, J. Clin.Invest. 52:2745, 1973; Hoshi, PNAS 81:6413, 1984). The diseaseenvironment present in chronic inflammatory diseases, such as Crohn'sdisease, differs from the normal condition by increased presence ofmultiple biologically active agents including IL-1, TNF-α, and IFN-γ(Woywodt, 1994; Kakazu, 1999). Other biologically active agents that maybe increased in chronic inflammatory disease environments include IL-4,IL-6, IL-8, IL-12, IL-13, IL-18, TGFbeta, and histamine, as well asactivated leukocytes and their products (Daig, 1996, Gut 38:216;Woywodt, 1994, Eur. cytokine Netw. 5:387; Kakazu, 1999 Am J.Gastroenterol. 94:2149; Pizarro, 1999, J. Immunol. 162:6829; Monteleone,1999, J. Immunol. 163:143; McClane, 1999 J Parenter Enteral Nutr 23:S20;Beck, 1999, Inflam. Bowel Dis. 5:44). Optimized assay combinations willcontain at least two, and preferably three, four or more of thesebiologically active agents. Concentrations of agents are standardaccording to the literature, typically at physiologic concentrations.Concentrations may also be determined experimentally as the amountrequired to saturate the relevant receptor. A useful feature of thepresent invention is that combinatorial effects of multiple factors areobserved over wide ranges of factor concentrations. Based on the factorsincluded in an assay combination, a set of parameters for including in abiomap are selected.

Selection of parameters is based on the following factors: 1) parametersthat are modulated in vivo in the disease environment or condition; 2)parameters that are modulated by one of the components in the assaycombination; 3) parameters that are modulated by more than one of thecomponents in the assay combination; 4) parameters that are modulated bythe combined action of two or more components in the assay combination;5) parameters that participate in the disease process, such as validateddisease targets; 6) cell surface and secreted molecules. Preferredparameters are functional and are downstream within signaling pathways,so as to provide information on effects of multiple pathways. For assaycombinations containing the factors TNFα, IFN-γ and IL-1, parametersexamined and chosen by these criteria include ICAM-1 (CD54), VCAM-1(CD106), E-selectin (CD62E), IL-8, HLA-DR and MIG (CLCX9). Otherparameters of interest for including in a Biomap include: IP-10,Eotaxin-1, Eotaxin-3, MCP-1, RANTES, Tarc, CD31, alphavbeta3, andP-selectin (CD62P). Parameters examined but not selected include: CD34,CD40, CD9, CXCR2, CD95, fibronectin, HLA-ABC, GROalpha, MCP-4, TAPA-1,alphaVbeta5, VE-Cadherin, CD44, von Willebrand factor, CD141, 142, 143,and CD151. Parameters are not selected for inclusion in a biomap for thefollowing reasons: redundancy, function of parameter is not associatedwith disease pathology, function is upstream in a signaling pathway,parameter is not modulated in response to factors, modulation is notrobust or reproducible. Cell death in inflammation, involved for examplein cellular remodeling in healing, as well as the consequences oftoxicity, involves apoptosis. Parameters of interest also includeparameters indicative of cell damage and apoptosis including releasedcytoplasmic lactate dehydrogenase (LDH) or mitochondrial cytochrome c,appearance of APO2.7 epitope or active caspase-3 (Zhang, J. Immunol.,157:3980, 1996; Bussing, Cytometry 37:133, 1999). Parameters indicativeof cell proliferation are also of interest and include Ki-67 and PCNA(Landberg, Cytometry, 13:230, 1992).

Strategies for optimizing the parameter set include: selecting only oneof any group of parameters that are co-regulated under all assaycombinations; preferentially selecting parameters that are functionallyrelevant to the disease process; preferentially selecting parametersthat give robust and reproducible results in multiple assays, or reflectcellular toxicity etc. In the present example, whereas both IP-10 andMIG are co-regulated under the assay conditions described, detection ofMIG by the cell-based ELISA as described above is more robust, thereforeMIG was preferentially included in the optimized set of parameters. Forparameter set optimization, additional parameters may be added to theinitial parameter set to distinguish assay combinations that result incellular de-adhesion, toxicity or other activity. Microscopicobservation can identify cellular de-adhesion, while release ofcytoplasmic substances, such as lactate dehydrogenase, can be measuredas an indication of toxicity. For example, CD31 is an endothelial celladhesion molecule that participates in cell-cell adhesion and completeloss of CD31 expression in an assay indicates loss of cells from theplate. Therefore, CD31 is a useful parameter for monitoring cellularde-adhesion.

The experiments provided in FIG. 1A-1C illustrate the usefulness of thepresent invention in compound screening applications. FIG. 1A shows thereadout patterns from confluent cultures of HUVEC incubated with eitherof TNF-α (5 ng/ml), IFN-γ (100 ng/ml) or IL-1 (1 ng/ml) or basal mediumfor 24 hours. After 24 hours, cultures are washed and evaluated for thepresence of the parameters ICAM-1 (1), VCAM-1 (2), E-selectin (3), IL-8(4), CD31 (5), HLA-DR (6) and MIG (7) by cell-based ELISA as described(Melrose, J. Immunol. 161:2457, 1998). For this, plates are blocked with1% Blotto for 1 hr, and treated with primary antibodies (obtained fromPharmingen and Becton Dickinson) at 1 ng/ml for 2 hr. After washing,secondary peroxidase-conjugated anti-mouse IgG antibody (Promega) at1:2500 is applied for 45 min. After washing, TMB substrate (Kierkegaard& Perry) is added and color developed. Development is stopped byaddition of H₂SO₄ and the absorbance at 450 nm (subtracting thebackground absorbance at 600 nm) is read with a Molecular Dynamics platereader. The relative expression levels of each parameter are indicatedby the OD at 450 nm shown along the y-axis. The mean +/−SD fromtriplicate samples is shown. The assay combinations shown in FIG. 1 areuseful in screening compounds that modulate TNF-α, IL-1 and IFN-γsignaling pathways, however, compounds must be evaluated separately inall three assay combinations to identify compounds that selectivelymodulate one or more of these pathways. In addition, compounds thatselectively modulate combinatorial effects of these pathways cannot bedistinguished.

An assay combination with improved usefulness is described in FIG. 1B.FIG. 1B shows the readout patterns from confluent cultures of HUVECcells treated with TNF-α (5 ng/ml), IFN-γ (100 ng/ml), TNF-α (5ng/ml)+IFN-γ (100 ng/ml) or base media. After 24 hours, cultures arewashed and evaluated for the presence of the parameters ICAM-1 (1),VCAM-1 (2), E-selectin (3), IL-8 (4), CD31 (5), HLA-DR (6) and MIG (7)by cell-based ELISA performed as described in FIG. 1 and are reported asthe OD at 450 nm in FIG. 2. The mean +/−SD from triplicate samples areshown. * indicates p<0.05 comparing results obtained with the twoseparate conditions. As shown in FIG. 2, HUVEC cultured with TNF-α for24 hours express increased levels of cell surface ICAM-1, VCAM-1, andE-selectin as measured by cell-based ELISA. HUVEC cultured with IFN-γfor 24 hours express increased levels of ICAM-1, HLA-DR and MIG. HUVECcultured in the presence of both TNF-α and IFN-γ for 24 hours produce acombined phenotype where HUVEC express increased levels of ICAM-1,VCAM-1, E-selectin, HLA-DR and MIG. This phenotype is more similar tothe in vivo phenotype of endothelial cells in chronic inflammation andmoreover reflects the stimulation of two different known pathways ofinterest in regulation of inflammatory processes. Concentrations ofTNF-α and IFN-γ employed and length of exposure are standard accordingto the literature. Concentrations and exposure length are also testedexperimentally and conditions chosen to achieve an endothelial cellphenotype displaying multiple features of endothelial cells in chronicinflammatory diseases (e.g. increased expression of ICAM-1, VCAM-1,E-selectin as well as HLA-DR and MIG). However, a particularly usefulfeature of the invention is that the combined phenotype is observed overa wide range of concentrations of the individual biologically activefactors. The results in FIG. 1B demonstrate how an assay combinationcontaining both TNF-α and IFN-γ is useful in screening for compoundsthat block either the TNF-α or IFN-γ signaling pathways, andfurthermore, can be used to distinguish compounds that modulatecombinatorial effects of these pathways.

Inclusion of additional biologically active factors further improves theusefulness of the screens provided in the present invention. FIG. 1Cshows the readout patterns from confluent cultures of HUVEC cellstreated with TNF-α (5 ng/ml)+IFN-γ (100 ng/ml) or TNF-α (5 ng/ml)+IFN-γ(100 ng/ml)+IL-1 (1 ng/ml). After 24 hours, cultures are washed andevaluated for the presence of the parameters ICAM-1 (1), VCAM-1 (2),E-selectin (3), IL-8 (4), CD31 (5), HLA-DR (6) and MIG (7) by cell-basedELISA performed as described in FIG. 1 and are reported as the OD at 450nm. The mean +/−SD from triplicate samples are shown. * indicates p<0.05comparing results obtained with the two separate conditions. Addition ofIL-1 to the assay combination containing TNF-α and IFN-γ results inincreased levels of E-selectin and IL-8 (shown in FIG. 1B), in additionto the increased levels of ICAM-1, VCAM-1, HLA-DR and MIG. E-selectinand IL-8 are particularly correlated with disease stage in chronicinflammatory diseases, including inflammatory bowel disease (MacDermott,1999, J. Clin. Immunol. 19:266; Koizumi, 1992, Gastroenterology1992103:840). Thus an assay combination containing IL-1, TNF-α and IFN-γrepresents an optimized assay combination. This assay combination isuseful for screening for compounds that modulate aspects of IL-1, TNF-αor IFN-γ signaling pathways. In particular, it provides a useful screenfor selecting compounds that are active when a particular target pathwaymay be modified by the activity of other pathways or when the target isnot known.

In subsequent panels one or more of IL-4, IL-6, IL-8, IL-12, IL-13,IL-18, TGFbeta, and histamine are applied; and/or neutralizingantibodies to autocrine factors such as IL-6, IL-1 and IL-8. Standardconcentrations of agents are employed as described in the literature.Based on the factors selected, a set of parameters for including in abiomap is selected.

Database of readout response patterns. A database of reference biomapsis compiled for the optimized assay combination and parameter set of theexample described in FIG. 1C. These reference biomaps are developed fromassay combinations in which specific modifications of the optimizedassay combination are made. These modifications included: 1) eliminationof one or more assay combination components, 2) addition of compounds orinterventions to the assay combination. Biological responses,particularly responses in primary human cells can display significantvariability from day to day and from donor to donor. One importantaspect of the present invention is that while absolute amounts ofparameters can vary substantially between assays, combinatorialresponses provide for less variability and the process of normalizationto produce a biomap provides cellular activity profiles that are robustand reproducible.

FIG. 2A shows a set of reference biomaps developed from assaycombinations in which one or more of the cytokines, IL1, TNF-α or IFN-γis eliminated. For each reference assay combination, the selectedparameters are measured and the resulting biomaps developed from thedata are compared. FIG. 2A shows how measuring the levels of theparameters ICAM-1 (1), VCAM-1 (2), E-selectin (3), IL-8 (4), CD31 (5),HLA-DR (6), and MIG (7), by cell-based ELISA under each of these assaycombinations, results in different reference biomaps for each assaycombination. The set of parameter measurements under each of theseconditions comprises a reference biomap to which test patterns can becompared. FIG. 2B shows a visual representation of this data, where themeasurement obtained for each parameter is classified according to itsrelative change from the value obtained in the optimized assaycombination (containing IL-1+TNF-α+IFN-γ), and represented by shadedsquares. For each parameter and assay combination, the square is coloredlight gray if the parameter measurement is unchanged (<20% above orbelow the measurement in the first assay combination (IL-1+TNF-α+IFN-γ))or p>0.05, n=3; white/gray hatched indicates that the parametermeasurement is moderately increased (>20% but <50%); white indicates theparameter measurement is strongly increased (>50%); black/gray hatchedindicates that the parameter measurement is moderated decreased (>20%but <50%); black indicates that the parameter measurement is stronglydecreased (>50% less than the level measured in the first assaycombination).

FIG. 2C shows an alternative visual representation of the set ofreference biomaps whereby individual parameter readouts are compared byhierarchical cluster analysis. For this, regression analysis isperformed on reference biomaps and correlation coefficients are used incluster analysis. The clustering relationships can be representedvisually, for example, as a tree in which related biomaps are on commonbranches, and the distance between patterns on the tree reflects theextent of differences in the biomaps. FIG. 2C shows how the biomapsderived from assay combinations containing TNF-α and/or IL1 are easilydistinguished from those derived from assay combinations containingIFN-γ or the combination of IFN-γ and TNF-α and/or IL-1. Applyingweighting factors to individual parameter readouts allows the biomapanalysis to sufficiently distinguish particular signaling pathways ofinterest. A significant aspect of the invention is the selection of aset of parameters and assay combinations that can optimally distinguishmultiple pathways of interest. Active compounds are chosen on the basisof their ability to alter the resulting biomap when included in aselected assay combination. Such alteration may include returning thelevels of one or more parameters to their levels in the basal condition,or otherwise altering the cellular responses, particularly when suchalterations reflect changes towards a desirable cellular state.

An inhibitor of TNF-α is an active compound in the optimized assaycombination described above. Addition of neutralizing anti-TNF-αantibodies to this assay combination results in reduced expressionlevels of ICAM-1, VCAM-1, E-selectin, IL-8, and MIG, and increasedexpression levels of CD31 (FIG. 3A). Confluent cultures of HUVEC cellsare treated with TNF-α (5 ng/ml)+IFN-γ (100 ng/ml)+IL-1 (1 ng/ml) in thepresence or absence of neutralizing anti-TNF-α or control antibody (Goatanti-IgG). After 24 hours, cultures are washed and evaluated for thecell surface expression of ICAM-1 (1), VCAM-1 (2), E-selectin (3), IL-8(4), CD31 (5), HLA-DR (6) and MIG (7) by cell-based ELISA performed asdescribed in FIG. 1. In FIG. 3A, the relative expression of eachparameter is shown along the y-axis as average value of the OD measuredat 450 nm of triplicate samples. The mean +/−SD from triplicate samplesare shown. * indicates p<0.05 comparing results obtained with anti-TNF-αto the control.

FIG. 3B, is a color-coded representation of the biomaps developed fromthe data shown in A. For each parameter and assay combination, thesquare is colored light gray if the parameter measurement is unchanged(<20% above or below the measurement in the first assay combination(IL-1+TNF-α+IFN-γ)) or p>0.05, n=3; white/gray hatched indicates thatthe parameter measurement is moderately increased (>20% but <50%); whiteindicates the parameter measurement is strongly increased (>50%);black/gray hatched indicates that the parameter measurement is moderateddecreased (>20% but <50%); black indicates that the parametermeasurement is strongly decreased (>50% less than the level measured inthe first assay combination).

Inhibitors of NFκB, MAP kinases and non-steroidal antiinflammatory drugsare active compounds in the optimized assay combination described above.FIG. 4A shows results of assaying confluent cultures of HUVEC cellstreated with TNF-α (5 ng/ml)+IFN-γ (100 ng/ml)+IL-1 (1 ng/ml) in thepresence or absence of (A) 10 μM NHGA, 200 μM PDTC or 9 μM PD098059 or(B) 125-500 μM ibuprofen. Compounds are tested at the highestconcentration at which they are soluble, and do not result in cellulartoxicity or loss of cells from the plate. After 24 hours, cultures arewashed and evaluated for the cell surface expression of ICAM-1 (1),VCAM-1 (2), E-selectin (3), IL-8 (4), CD31 (5), HLA-DR (6) and MIG (7)by cell-based ELISA performed as described in FIG. 1. A color-codedrepresentation of the biomaps developed from the data is shown. For eachparameter and assay combination, the square is colored light gray if theparameter measurement is unchanged (<20% above or below the measurementin the first assay combination (IL-1+TNF-α+IFN-γ)) or p>0.05, n=3;white/gray hatched indicates that the parameter measurement ismoderately increased (>20% but <50%); white indicates the parametermeasurement is strongly increased (>50%); black/gray hatched indicatesthat the parameter measurement is moderately decreased (>20% but <50%);black indicates that the parameter measurement is strongly decreased(>50% less than the level measured in the first assay combination).

In the present example, FIG. 4A shows how addition of the NFκBinhibitors nordihydroguaiaretic acid (NHGA) (Brennen, Biochem.Pharmacol., 55:965, 1998) or pyrrolidine dithiocarbamate (PDTC) (Boyle,Circulation, 98, (19 Suppl):II282, 1998) to the optimized assaycombination results in altered biomaps that are distinct from thealtered biomaps obtained with the p42/44 MAP kinase inhibitor, PD098059(Milanini, J. Biol. Chem. 273:18165, 1998). Active compounds that actwith a similar mechanism of action as NHGA and PDTC will give a biomapthat can be distinguished from active compounds that act with a similarmechanism of action as PD098059.

Obtaining biomaps from drug compounds tested at different concentrationsalso expands the usefulness of the database. In the present example,ibuprofen gives visually biomaps when tested at 500, 250 and 125 μM, asshown in FIG. 4B, although regression analysis indicates they are highlyrelated (correlation coefficients derived from the primary data rangebetween 0.96-0.99).

Reference biomaps from assay combinations that include known drugcompounds, agents, or with other specific modifications are developedfor inclusion in a database. Biomaps from these assay combinations aredeveloped so as to expand the usefulness of the database. Table 1 showsa list of agents or specific modifications evaluated, includingN-acetylcysteine (Faruqui, Am. J. Physiol. 273(2 Pt 2):H817, 1997), thecorticosteroids dexamethasone and prednisolone, echinacea, AA861 (Lee,J. Immunol. 158, 3401, 1997), apigenin (Gerritsen, Am. J. Pathol.147:278, 1995), nordihydroguaiaretic acid (NHGA) (Brennen, Biochem.Pharmacol., 55:965, 1998), phenylarsine oxide (PAO) (Dhawan, Eur. J.Immunol. 27:2172, 1997), pyrrolidine dithiocarbamate (PDTC) (Boyle,Circulation, 98, (19 Suppl):II282, 1998), PPM-18 (Yu, Biochem. J.,328:363, 1997), the non-steroidal anti-inflammatory drug (NSAID)buprofen, SB 203580, PD098059 (Milanini, J. Biol. Chem. 273:18165,1998), AG126 (Novogrodsky, Science 264, 1319, 1994), and neutralizinganti-TNF-α antibody. Color-coded representations of the resultingbiomaps are shown. Confluent cultures of HUVEC cells are treated withTNF-α (5 ng/ml)+IFN-γ (100 ng/ml)+IL-1 (1 ng/ml) in the presence orabsence of agents or buffers at the concentrations indicated in Table 1.Compounds are obtained from commercial sources and prepared in asuitable buffer (water, base media, DMSO, methanol or ethanol). After 24hours, cultures are washed and evaluated for the cell surface expressionof ICAM-1 (1), VCAM-1 (2), E-selectin (3), IL-8 (4), CD31 (5), HLA-DR(6) and MIG (7) by cell-based ELISA performed as described in FIG. 1. Acolor-coded representation of the resulting biomaps developed from thedata is shown. For each parameter and assay combination, the square iscolored light gray if the parameter measurement is unchanged (<20% aboveor below the measurement in the control assay combination(IL-1+TNF-α+IFN-γ)) or p>0.05, n=3; white/gray hatched indicates thatthe parameter measurement is moderately increased (>20% but <50%); whiteindicates the parameter measurement is strongly increased (>50%);black/gray hatched indicates that the parameter measurement ismoderately decreased (>20% but <50%); black indicates that the parametermeasurement is strongly decreased (>50% less than the level measured inthe first. Control assay combinations for each agent include anappropriate concentration of the diluent buffer. TABLE 1 Referencebiomaps.

FIG. 4C shows a visual representation of how these reference biomaps canbe compared by pattern similarity and cluster analysis. Readout patternsare analyzed by hierarchical clustering techniques, and are visualizedas a tree diagram in which a) each terminal branch point represents thereadout pattern from one assay combination in one experiment; b) thelength of the vertical distance from the upper horizontal line (nochange and control patterns) to the termini are related to the extent ofdifference in the readout pattern from the control environment pattern;and c) the distance along the branches from one terminal pattern valueto another reflects the extent of difference between them. Similarpatterns are thus clustered together.

Compounds that inhibit the NFκB pathway, such as the 5-lipoxygenaseinhibitors AA861 and nordihydroguaiaretic acid (NHGA) (Lee, J. Immunol.158, 3401, 1997), pyrrolidine dithiocarbamate (PDTC) (Boyle, Circulation98:(19 Suppl):II282, 1998), PPM-18, a chemically synthesizednaphthoquinone derivative (Yu, Biochem. J., 328:363, 1997) and theflavenoid apigenin (Gerritsen, Am. J. Pathol. 147:278, 1995), havesimilar reference biomaps and cluster together. The corticosteroids,dexamethasone and prednisolone also yield a set of related referencebiomaps that are distinct from those of NFκB pathway inhibitors.

An important feature of biomap analysis is how biomaps resulting fromdifferent concentrations of active agents, although they differ from oneanother (see FIG. 4C), remain clustered together in the clusteranalysis. This can be seen in FIG. 4C where the biomaps that result fromtesting PD098059 at different concentrations remain in the same cluster(indicating their similarity with one another), although biomapsresulting from testing PD098059 at higher concentrations are found inthe lower branches of the cluster, indicating higher degree ofdifference (lower correlation coefficient) from the biomaps resultingfrom no intervention or inactive agents. Thus biomap analysis is usefulfor distinguishing the mode of action of a variety of compounds.

This example demonstrates that the biomaps are useful in distinguishingthe mode of action of candidate compounds, so as to know whethercombinations of candidate compounds act on the same pathway or differentpathways, their combined effect on parameter levels and whether theyprovide synergy or act in an antagonistic way.

These assay combinations are highly useful for testing a large number ofcompounds or agents with many different or unknown mechanisms of action.This procedure balances the desirability of a screening assay thatprovides in depth information, with the advantages of an assay that isalso amenable for scale-up high throughput screening. The assaycombinations described are useful for general screening for compoundswith anti-inflammatory or proinflammatory activity. Assay combinationstailored for specific inflammatory diseases are developed by alteringthe combination of input biologically active agents. For example,specific assay combinations useful for inflammatory diseases that aremore Th2-like in nature, such as asthma or allergy should includeadditional agents, such as IL-4 or IL-13, that are preferably found inthose disease conditions, and so forth.

Example 2 Multiplex Assay Combinations for Distinguishing Mechanism ofAction

The following example demonstrates the utility of the invention inidentification of the mechanism of action of a test compound orintervention identified in the optimized assay combination of Example 1.This assay combination is included in a panel that contains specific andtargeted alterations. A neutralizing antibody to TNF-α was selected as atest agent, as it is active when tested in the optimized primary assaycombination of Example 1 (FIG. 3A). When the test agent is evaluated inthe panel of assay combinations, it can be determined if the activecompound is acting on a component(s) unique to one receptor-stimulatedpathway, or on a common pathway component or pathway activity. Theneutralizing antibody to TNF-α as a test agent evaluated in these assaycombinations alters the biomap, as shown in FIG. 3B.

Confluent cultures of HUVEC cells are treated with TNF-α (5 ng/ml),IFN-γ (100 ng/ml), IL-1 (20 ng/ml), the combination of TNF-α+IFN-γ+IL-1,or media (no cytokine) in the presence or absence of neutralizinganti-TNF-α, 20 μM AA861 or 10 μM NHGA. After 24 hours, cultures arewashed and evaluated for the cell surface expression of ICAM-1 (1),VCAM-1 (2), E-selectin (3), IL-8 (4), and MIG (5) by cell-based ELISAperformed as described in FIG. 1. A color-coded representation of theresulting biomaps derived from the data is shown in FIG. 5, coded asdescribed in FIG. 2B.

These data demonstrate expression of the biomap from the assaycombination containing TNF-α alone is altered, but not the biomap in theassay combinations that contain IL-1 or IFN-γ alone. This resultdemonstrates that the test agent acts on the TNF-α pathway but not onthe IL-1 or IFN-γ pathways. FIG. 5 also shows the test compound isdistinguished from active compounds that target multiple cytokinesignaling pathways, such as the NFκB inhibitors, NHGA and AA861.

The mechanism of action of the test agent is accomplished when identicalbiomaps are obtained from assay combinations containing the test agentand assay combinations generated from known specific alterations of theassay combination. Eliminating the cytokine TNF-α from the primary assaycombination results in the same biomap as the assay combinationcontaining the test agent, the neutralizing TNF-α antibody.

Confirmation is performed by evaluating the test agent in assaycombinations that include both physiologic and alternative pathwayactivators. Confluent cultures of HUVEC cells are treated with TNF-α (5ng/ml), IL-1 (20 ng/ml), an activating antibody against the TNF-αreceptor p55, or media. After 24 hours, cultures are washed andevaluated for the cell surface expression of ICAM-1 (1), VCAM-1 (2),E-selectin (3), CD31 (4), and MIG (5) by cell-based ELISA performed asdescribed in FIG. 1. A color-coded representation of the resultingbiomap, prepared from the data is shown in FIG. 6, coded as described inFIG. 2B.

FIG. 6 shows that among the physiologic and alternative activators ofthe TNF-α pathway, the biomaps resulting from cultures containing eitherIL-1 or an activating antibody to p55 are not sensitive to the testagent, whereas the biomap resulting from cultures containing TNF-α issensitive. As TNF-α is the most upstream component of the TNF-α pathwaythat is sensitive to the test agent, it is involved in the targetpathway step of the test agent.

Example 3 Analysis in Multiplex Assay Combinations for IdentifyingMechanism of Action

The following example demonstrates the usefulness of the presentinvention for identification of mechanism of action of a test agentselected as an active agent. A recombinant fusion protein containing theextracellular domains of the p55 TNF-α-receptor fused to immunoglobulinFc domain (p55-Fc fusion protein) is selected as an active compound whentested in the optimized assay combination of Example 1 (FIG. 7A).

Confluent cultures of HUVEC cells are treated with TNF-α (5 ng/ml)+IFN-γ(100 ng/ml)+IL-1 (20 ng/ml) in the presence or absence of p55-Fc (50ng/ml). After 24 hours, cultures are washed and evaluated for the cellsurface expression of ICAM-1 (1), VCAM-1 (2), E-selectin (3), CD31 (4),and MIG (5) by cell-based ELISA performed as described in FIG. 1. Therelative expression of each parameter is shown in FIG. 7A along they-axis as average value of the OD measured at 450 nm of triplicatesamples. The mean +/−SD from triplicate samples are shown. * indicatesp<0.05 comparing results obtained with p55-Fc to the control.

In FIG. 7B, confluent cultures of HUVEC cells are treated with TNF-α (5ng/ml), IFN-γ (100 ng/ml), IL-1 (20 ng/ml), the combination ofTNF-α+IFN-γ+IL-1, or media in the presence or absence of p55-Fc. After24 hours, cultures are washed and evaluated for the cell surfaceexpression of ICAM-1 (1), VCAM-1 (2), E-selectin (3), IL-8 (4), and MIG(5) by cell-based ELISA performed as described in FIG. 1. A color-codedrepresentation of the resulting biomaps prepared from the data is shownin FIG. 7B, coded as described in FIG. 2B.

In FIG. 7C, confluent cultures of HUVEC cells are treated with TNF-α (5ng/ml), IL-1 (20 ng/ml), an activating antibody against the TNF-αreceptor p55, or media. After 24 hours, cultures are washed andevaluated for the cell surface expression of ICAM-1 (1), VCAM-1 (2),E-selectin (3), CD31 (4), and MIG (5) by cell-based ELISA performed asdescribed in FIG. 1. A color-coded representation of the resultingbiomaps prepared from the data is shown in FIG. 7C, coded as describedin FIG. 2B.

The p55-Fc fusion protein as a test agent evaluated in these assaycombinations, alters the biomap, as shown in FIG. 7B. The biomap in theassay combination containing TNF-α alone is altered, but not the biomapin the assay combinations that contain IL-1 or IFN-γ alone. This resultdemonstrates that the test agent acts on the TNF-α pathway but not onthe IL-1 or IFN-γ pathways.

FIG. 7C shows that among the physiologic and alternative activators ofthe TNF-α pathway, the biomap from IL-1 is not sensitive to the testagent, whereas the biomap from TNF-α or an activating antibody to thep55 is sensitive to the test agent. As the TNF-α-receptor p55 is themost upstream component of the TNF-α pathway that is sensitive to thetest agent, it is a component of the target step of the test agent.

Example 4 Analysis in Multiplex Assay Combinations for IdentifyingMechanism of Action

The following example demonstrates the usefulness of the presentinvention for identification of mechanism of action of test agents thathave proinflammatory activities. An activating antibody toTNF-α-receptor p55 (Act-anti-p55) is an active compound when tested inan assay combination containing confluent HUVEC cultured in a basalmedium for 24 hours (FIG. 8, “no cytokine” assay combination), sinceAct-anti-p55 alters the biomap of this assay combination resulting inincreased levels of the readout parameters ICAM-1 (1), E-selectin (2)and VCAM-1 (3), and reduced levels of the readout parameter CD31 (4).

For identifying the mechanism of action and determining the cellulartarget, the test compound is evaluated in secondary or “decoding” assaycombinations. These combinations contain the test agent as well as knownregulators of the modulated parameters. For the parameters ICAM-1,VCAM-1 and E-selectin, known modulators include IL-1 and TNF-α (ICAM-1,VCAM-1 and E-selectin); and IFN-γ (ICAM-1 and VCAM-1).

Confluent cultures of HUVEC cells are treated with TNF-α (5 ng/ml),IFN-γ (100 ng/ml), IL-1 (20 ng/ml), the combination of TNF-α+IFN-γ+IL-1,or media in the presence or absence of Act-anti-p55, After 24 hours,cultures are washed and evaluated for the cell surface expression ofICAM-1 (1), VCAM-1 (2), E-selectin (3), IL-8 (4), and MIG (5) bycell-based ELISA performed as described in FIG. 1. A color-codedrepresentation of the resulting biomaps prepared from the data is shownin FIG. 8, coded as described in FIG. 2B.

FIG. 8 shows that the test agent alters the biomaps derived of assaycombinations containing IL-1 or IFN-γ, but not the biomaps resultingfrom assay combinations containing TNF-α. This result indicates that thetest compound acts through a pathway that is distinct from the IL-1 andIFN-γ pathways but that cannot be distinguished from the TNF-α pathwayin these assay combinations. To confirm that the test compound actsthrough the TNF-α pathway, and to identify the pathway step targeted bythe test agent, the test agent is evaluated in assay combinations thatcontain known inhibitors of the TNF-α pathway. The recombinant fusionprotein, p55-Fc, is an example of a known inhibitor of the TNF-αpathway.

As shown in FIG. 9, confluent cultures of HUVEC cells are treated withAct-anti-p55 in the presence or absence of p55-Fc. After 24 hours,cultures are washed and evaluated for the cell surface expression ofICAM-1 (1), VCAM-1 (2), E-selectin (3), IL-8 (4), CD31 (5), HLA-DR (6)and MIG (7) by cell-based ELISA performed as described in FIG. 1. FIG. 9shows the relative expression (y-axis) of each parameter (x-axis) asaverage value of the OD measured at 450 nm of triplicate samples. Themean +/−SD from triplicate samples are shown. * indicates p<0.05comparing results obtained with Act-anti-p55 or Act-anti-p55+p55-Fc tothe Control.

As shown in FIG. 9, p55-Fc fusion protein, a soluble form of the p55TNF-α receptor, that blocks TNF-α binding to the TNF-α receptor, altersthe biomap generated by the test agent. This demonstrates that thepathway step targeted by the test agent is upstream or includes the p55TNF-α receptor. Since a neutralizing antibody to human TNF-α does notalter the biomap generated by the test agent, the target pathway step ofthe test agent does not include human TNF-α.

Example 5 Drug Interaction Screening

The present invention is useful for analysis of combinatorial druginteractions. Drug interactions occur if the presence of two drugsproduces a readout response pattern, or biomap, different from thoseproduced by either compound alone in an assay combination. Drugs may acton independent molecular targets within the cell but nonetheless producea combined cellular phenotype that is distinct and potentiallyphysiologically or therapeutically different in its effects. Drugcombinations may have synergistic or counteracting effects, in which onecompound enhances or suppresses the effects of another on a parameter orparameters, or alters the dose response, and may imply a more complexdrug interaction at the level of intracellular pathways. Interaction maybe beneficial if resulting combined activity is desirable;alternatively, interaction may be detrimental if the resulting combinedactivity is undesirable. The desirability of a particular druginteraction activity depends on the context. For example, a drugcombination that results in increased toxicity compared to either drugalone may be undesirable for an anti-inflammatory therapeutic, butdesirable for a cancer therapeutic. The present invention is highlyuseful for distinguishing combinatorial drug activities since the assaycombinations described are designed to measure the outcome of multiplesignaling pathways and their interactions.

A neutralizing antibody to TNF-α and a neutralizing antibody to IL-1 areboth active compounds when screened in the optimized assay combinationof Example 1 (FIG. 10A). Confluent cultures of HUVEC cells are treatedwith TNF-α (5 ng/ml)+IFN-γ (100 ng/ml)+IL-1 (20 ng/ml) in the presenceor absence of neutralizing antibodies to IL-1, TNF-α or the combination.Antibody concentrations are in excess as increased concentrations ofantibodies does not further alter the biomaps. After 24 hours, culturesare washed and evaluated for the cell surface expression of ICAM-1 (1),VCAM-1 (2), E-selectin (3), IL-8 (4), CD31 (5), HLA-DR (6) and MIG (7)by cell-based ELISA performed as described in FIG. 1. In FIG. 10A therelative expression of each parameter is shown along the y-axis asaverage value of the OD measured at 450 nm of triplicate samples. Themean +/−SD from triplicate samples are shown. FIG. 10B shows acolor-coded representation of the resulting biomaps prepared from thedata coded as described in FIG. 2B. FIG. 10B demonstrates that whensaturating concentrations of the neutralizing antibodies to TNF-α andIL-1 are included together in the assay combination, a biomap isobtained that is different from the biomap obtained by assaycombinations containing each test agent individually, even though thetest agents are provided at saturating (excess) concentrations.Compounds that result in similar biomaps are diagnostic of inhibitorsthat target both the IL-1 and TNF-α pathways. The present systemtherefore provides an assay system for screening for and distinguishingsuch inhibitors.

Example 6 Drug Interaction Screening

The present invention is useful for the identification of druginteractions or drug combinations that are beneficial. For the presentexample, the NFκB inhibitor PPM-18 (at 2 μM) (Yu, Biochem. J. 328:363,1997) and the tyrosine kinase inhibitor AG126 (25 μM) (Novogrodsky,Science 264, 1319, 1994) are both active compounds when screened in theassay combination of Example 1 (FIG. 11A). Confluent cultures of HUVECcells are treated with TNF-α (5 ng/ml)+IFN-γ (100 ng/ml)+IL-1 (20 ng/ml)in the presence or absence of the tyrphostin AG126 (25 μM), PPM-18 (2μM) or the combination. After 24 hours, cultures are washed andevaluated for the cell surface expression of ICAM-1 (1), VCAM-1 (2),E-selectin (3), IL-8 (4), CD31 (5), HLA-DR (6) and MIG (7) by cell-basedELISA performed as described in FIG. 1. The relative expression of eachparameter is shown in FIG. 11A as average value of the OD measured at450 nm of triplicate samples. The mean +/−SD from triplicate samples areshown. FIG. 11B shows a color-coded representation of the resultingbiomaps prepared from the data, coded as described in FIG. 2B.

In this example, higher concentrations of either drug (2-fold) whentested alone result in cellular toxicity. Together, however, thecombination of PPM-18 and AG126 at non-toxic concentrations produces acombined cellular phenotype that is additive for the effect on biomapparameters, but is not toxic to cells (FIG. 11B). The present system,therefore, provides an assay system for screening for compounds thatsynergize with inhibitors of NFκB, or with tyrosine kinase inhibitors toproduce a desirable phenotype, without resulting in cellular toxicity.

Example 7 Regulators of Endothelial Cell Responses to AllergicInflammation

The present invention is applied for the screening of compounds for usein treating allergic inflammatory responses such as allergy, asthma,atopic dermatitis and chronic inflammatory diseases disposed towardsaTh2 phenotype or modulation of Th2 type immune responses.

Primary human umbilical vein endothelial cells (HUVEC) are used. Othercells that may replace HUVEC in the screen include primary microvascularendothelial cells, aortic or arteriolar endothelial cells or endothelialcell lines such as EAhy926 or E6-E7 4-5-2G cells or human telomerasereverse transcriptase-expressing endothelial cells (Simmons, J.Immunol., 148:267, 1992; Rhim, Carcinogenesis 19:673, 1998; Yang, J.Biol. Chem. 274:26141, 1999). 2×10⁴ cells/ml are cultured to confluencein EGM-2 (Clonetics). Other media that may replace EGM-2 include EGM(Clonetics) and Ham's F12K medium supplemented with 0.1 mg/ml heparinand 0.03-0.05 mg/ml endothelial cell growth supplement (ECGS) and 10%FBS, or medium M199 (Life Technologies, Inc.) containing 20% fetalbovine serum and 2 ng/ml basic fibroblast growth factor (Jaffe, J. Clin.Invest. 52:2745, 1973; Hoshi, PNAS 81:6413, 1984).

The following are then applied for 24 hours: IL-4 (20 ng/ml), HIS (10μM) and TNF-α (5 ng/ml). In subsequent panels one or more of IL-1 (1ng/ml), IFNγ, (100 ng/ml) IL-13 (20 ng/ml), mast cell tryptase, orfibronectin are added to the initial three factors or may replace one ofthe factors. Standard concentrations of agents are employed as describedin the literature. Based on the parameters altered by the indicatedfactors, biomaps are generated for the parameters ICAM-1, VCAM-1,E-selectin, IL-8, CD31, P-selectin and Eotaxin-3. Other markers ofinterest for adding to the biomap include: Eotaxin-1, HLA-DR, MIG, Tarc,MCP-1, and IL-8. FIG. 12 shows biomaps resulting from confluent culturesof HUVEC cells treated with IL-4 (20 ng/ml), HIS (10 μM), TNF-α (5ng/ml), and/or media. After 24 hours, cultures are washed and evaluatedfor the presence of the parameters ICAM-1 (1), VCAM-1 (2), E-selectin(3), IL-8 (4), CD31 (5), P-selectin (6) and eotaxin-3 (7) by cell-basedELISA performed as described in FIG. 1. FIG. 12 shows a visualrepresentation of the resulting biomaps prepared from the data, wherethe measurement obtained for each parameter is classified according toits relative change from the value obtained in the assay combinationcontaining IL-4+TNF-α+HIS, and represented by shaded squares. For eachparameter and assay combination, the square is gray if the parametermeasurement is unchanged (<20% above or below the measurement in thefirst assay combination (IL-4+TNF-α+HIS)) or p>0.05, n=3; white/grayhatched indicates that the parameter measurement is moderately increased(>20% but <50%); white indicates the parameter measurement is stronglyincreased (>50%); black/gray hatched indicates that the parametermeasurement is moderated decreased (>20% but <50%); black indicates thatthe parameter measurement is strongly decreased (>50% less than thelevel measured in the first assay combination).

A database of biomaps is generated from a panel of assay combinationsthat include the presence and absence of each biologically activefactor; and reference drugs or agents including inhibitors of signalingpathways such as NFkB and STAT inhibitors, anti-histamine or histaminereceptor antagonists; as well as immune stimulatory agents includingpathogens or pathogen components, that are screened and biomapsgenerated that show the changes in the markers with the differentagents. Many agents are given in The Pharmacologic Basis ofTherapeutics. The biomaps with the known agents are used to compare tocandidate agents. This allows the recognition of the pathway(s) thecandidate agent acts on, by comparing the changes in the level of thespecific markers for known drugs affecting known pathways and thechanges observed with the candidate agent. In addition to further add tothe utility of the biomap, one may include in the database referencebiomaps generated from assay panels containing cells with geneticconstructs that selectively target or modulate specific cellularpathways (e.g. NFκB, MAP kinase, etc), or cells that contain knowngenetic mutations.

Example 8 Regulators of Epithelial Cell Responses to Inflammation

The present invention is applied for the screening of compounds thatregulate epithelial cell responses to inflammation.

Normal human epithelial keratinocytes (NHEK) (5×10⁴ cells/ml) arecultured to 80% confluence in serum free Keratinocyte Basal Medium-2(Clonetics CC3103) supplemented with BPE (30 μg/ml), hEGF (100 ng/ml),insulin (5 μg/ml), transferrin (10 μg/ml), and epinephrine (500 ng/ml).Other cells that may substitute for NHEK include the spontaneouslyimmortalized keratinocyte cell line, HaCaT(Boukamp, J. Cell Biol.106:761, 1988), normal human lung epithelial cells (NHLE), renal,mammary and intestinal epithelial cells. One or more of the followingare applied for 48 hours: IFN-γ (50 ng/ml), TNF-α (50 ng/ml) and IL-1(20 ng/ml). Based on the parameters altered by the indicated factors,biomaps are generated for the parameters MIG, ICAM-1, CD44, IL-8, Mip-3alpha (CCCL20), MCP-1, and E-Cadherin. Other parameters of interest forincluding in biomaps are: CD40, IP-10, EGF-Receptor, IL-6, IL-15Ralpha,CD1d, CD80, CD86, TARC, eotaxin-1, eotaxin-3, HLA-DR and CD95. Otherfactors of interest for including in assay combinations include TGFβ,IL-9, GM-CSF, CD40L and IL-17 activities.

In FIG. 13, confluent cultures of NHEK cells are treated with one ormore of IFN-γ (50 ng/ml), TNF-α (50 ng/ml), IL-1 (20 ng/ml) and/or basemedia. After 48 hours, cultures are washed and evaluated for thepresence of the parameters Mig (1), ICAM-1 (2), CD44 (3), IL-8 (4),Mip-3 alpha (5), MCP-1 (6), and E-Cadherin (7) by cell-based ELISAperformed as described in FIG. 1. FIG. 13 shows a visual representationof the resulting biomaps prepared from the data, where the measurementobtained for each parameter is classified according to its relativechange from the value obtained in the assay combination containingIL-1+IFNγ, and represented by shaded squares. For each parameter andassay combination, the square is gray if the parameter measurement isunchanged (<20% above or below the measurement in the first assaycombination (IL-1+IFN-γ)) or p>0.05, n=3; white/gray hatched indicatesthat the parameter measurement is moderately increased (>20% but <50%);white indicates the parameter measurement is strongly increased (>50%);black/gray hatched indicates that the parameter measurement is moderateddecreased (>20% but <50%); black indicates that the parametermeasurement is strongly decreased (>50% less than the level measured inthe first assay combination).

A database of biomaps is generated from a panel of assay combinationsthat include the presence and absence of each biologically activefactor; and reference drugs or agents including inhibitors of signalingpathways such as NFkB and STATs, as well as immune stimulatory agentsincluding pathogens or pathogen components, that are screened andbiomaps generated that show the changes in the markers with thedifferent agents. Many agents are given in The Pharmacologic Basis ofTherapeutics. The biomaps with the known agents are used to compare tocandidate agents. This allows the recognition of the pathway(s) thecandidate agent acts on, by comparing the changes in the level of thespecific markers for known drugs affecting known pathways and thechanges observed with the candidate agent. In addition to further add tothe utility of the biomap, one may include in the database referencebiomaps generated from assay panels containing cells with geneticconstructs that selectively target or modulate specific cellularpathways (e.g. NFkB, MAP kinase, etc), or cells that contain knowngenetic mutations.

Example 9 Regulators of T Cell Responses T Cell-Endothelial CellCo-Cultures

The present invention is applied for the screening of compounds foraltering immune and/or inflammatory conditions that involve T cells.

Primary human umbilical vein endothelial cells and the human T cellline, KIT255 are used. Other cells that may replace HUVEC in the screeninclude primary microvascular endothelial cells or aortic endothelialcells. 2×10⁴ HUVEC/ml were cultured to confluence in EGM-2 (Clonetics).Other media that may replace EGM-2 include EGM (Clonetics) and Ham'sF12K medium supplemented with 0.1 mg/ml heparin and 0.03-0.05 mg/mlendothelial cell growth supplement (ECGS) and 10% FBS, or medium M199(Life Technologies, Inc.) containing 20% fetal bovine serum and 2 ng/mlbasic fibroblast growth factor (Jaffe, J. Clin. Invest. 52:2745, 1973;Hoshi, PNAS 81:6413, 1984). One or more of the following are thenapplied: 10³ KIT255 cells, IL-2 (10 ng/ml), IL-12 (10 ng/ml), and orbase media. After 24 hours, cultures are washed and evaluated for thepresence of the parameters ICAM-1 (1), VCAM-1 (2), E-selectin (3), IL-8(4), CD31 (5), HLA-DR (6) and MIG (7) by cell based ELISA as describedin FIG. 1 and shown in FIG. 14. In this figure, analysis performed bycell based ELISA provides readout patterns that combine HUVEC and T cellreadouts. FIG. 14 demonstrates that the biomaps derived from assaycombinations containing KIT255 cells +/−IL-2 and IL-12 can bedistinguished. Other cells that may replace KIT255 include humanperipheral blood leukocytes, human peripheral blood T cells, humanperipheral blood CD3+ cells, and the human T cell lines Jurkat andHUT78. In subsequent panels, one or more of: PHA, IL-6, IL-7, activatingantibody to CD3, activating antibody to CD28, IL-1, TNF-α, IFN-γ, IL-4,IL-13 or neutralizing antibodies to IL-1, IL-2, TNF-α, IFN-γ, IL-12and/or IL-4 are applied. Other markers of interest for adding to thebiomap include MCP-1, IP-10, cutaneous lymphocyte antigen (CLA), CXCR3,CCR3, TNF-α, IFN-γ, IL-2, IL-4, alpha4beta7, alphaEbeta7, andL-selectin. Analytical methods that distinguish T cells from endothelialcells, such as flow cytometry or image analytical techniques can beemployed. A database of biomaps is generated from a panel of assaycombinations that include anti-inflammatory drug compounds includinginhibitors of T cell activation and/or T cell proliferation, calcineurininhibitors, etc. are screened and biomaps are generated that reflect thechanges in the markers with the different agents. Such agents are givenin The Pharmacologic Basis of Therapeutics. The biomaps with the knownagents are used to compare to candidate immunomodulatory agents. Thisallows the recognition of the pathway(s) the candidate test agent actson, by comparing the changes in the level of the specific markers forknown agents affecting known pathways and the changes observed with thecandidate test agent. In addition to further add to the utility of thebiomap, one may include in the database reference biomaps generated fromassay panels containing cells with genetic constructs that selectivelytarget or modulate specific cellular pathways (e.g. NFAT, calcineurin,NFκB, MAP kinase, etc), or cells that contain known genetic mutations,e.g. Jurkat cell lines that lack Ick, CD45, etc. (Yamasaki, J. Biol.Chem. 272:14787, 1997).

Example 10 Function of Genes in Cellular Responses in Inflammation

The present invention is useful for identifying functions of genes andtheir expressed gene products. For example, genes whose productsregulate inflammation can be identified in an inflammation model usinghuman endothelial cells as an indicator cell type. A panel of assaycombinations that reproduce aspects of the response of the endothelialcells to different types of inflammatory processes is used, as describedin Example 1.

Primary human umbilical vein endothelial cells (HUVEC) are used. Othercells that may replace HUVEC in the screen include primary microvascularendothelial cells, aortic or arteriolar endothelial cells or endothelialcell lines such as EAhy926 or E6-E7 4-5-2G cells or human telomerasereverse transcriptase-expressing endothelial cells (Simmons, J.Immunol., 148:267, 1992; Rhim, Carcinogenesis 19:673, 1998; Yang, J.Biol. Chem. 274:26141, 1999). Endothelial cells in exponential growthphase are transduced with retroviral vectors or tranfected with plasmidvectors encoding test genes. A marker gene is incorporated in the vectorthat allows monitoring of expression. A suitable retroviral vector isdescribed in FIG. 15, and is derived from the MoMLV-based pFB vector(Stratagene). Other standard methods for transduction or transfection ofcells for expression of genes can be substituted.

Test genes are inserted downstream of the MoMLV LTR. The marker gene isthe truncated form of the human nerve growth factor receptor (NGFR))(Mavilio, Blood 83:1988, 1994) separated from the test gene by anindependent ribosomal entry site sequence (IRES). The IRES is 100 bpfragment from human eIF4G IRES sequence (Gan, J. Biol. Chem. 273:5006,1988). In the example shown in FIG. 16, the test gene used is human Ikappa B-related Bcl-3 (Dechend, Oncogene, 18:3316, 1999). Retroviralvector plasmid DNA is transfected into AmphoPack-293 cells (Clonetech)by modified calcium phosphate method according to manufacturer'sprotocol (MBS transfection kit, Stratagene). Cell supernatants areharvested 48 hours post-transfection, filtered to remove cell debris(0.45 μm) and transferred onto exponentially growing HUVEC cells. DEAEdextran (conc 10 μg/ml) is added to facilitate vector transduction.After 5-8 hour incubation the viral supernatant is removed and cellscultured for an additional 40 hours. Gene transfer efficiency isdetermined by FACS using NGFR-specific monoclonal antibodies, and in theexperiment shown, is ≧90%. Transduced cells are re-plated into 96-wellplates, and cultured to confluence for biomap analysis.

Confluent transduced or control HUVEC cells are treated with thecombination of TNF-α (5 ng/ml)+IFN-γ (100 ng/ml)+IL-1 (1 ng/ml); or withTNF-α (5 ng/ml), IL-1 (1 ng/ml) or media only. After 24 hours, culturesare washed and evaluated for the cell surface expression of ICAM-1 (1),VCAM-1 (2), E-selectin (3), IL-8 (4), CD31 (5), HLA-DR (6) and MIG (7)as under Example 1. FIG. 16 shows how over-expression of Bcl-3 resultsin altered biomaps in the assay panel used. Expression of Bcl-3 resultsin increased expression of ICAM-1 in endothelial cells under basalconditions, and enhanced expression of ICAM-1 and VCAM-1 in cellscultured with IL-1. It does not alter the biomaps resulting from assaycombinations containing TNFα or TNF-α+IFN-γ+IL-1. FIG. 16 shows a visualrepresentation of the biomaps derived from the resulting data. Thusexpression of Bcl-3 yields a distinctive biomap in the assay panelemployed. It can be concluded from this biomap panel that bcl-3 altersthe basal reference bioamp and the biomap from IL1, but not that fromTNF. The results define bcl-3 as a potential target for modulation ofthe inflammatory response.

This example demonstrates that the biomap analysis is useful foridentifying gene function. In this particular case the biomap analysisshows that Bcl-3 is involved in regulating expression of ICAM-1 andVCAM-1, and thus inflammatory states. Furthermore biomap analysisidentifies cellular states in which gene functions alter cellularresponses (e.g. IL-1 versus TNF biomap). Information about the functionof unknown genes is obtained by comparing biomaps of unknown genes tothe distinctive biomaps determined by the known gene products, drugs,antibodies, and other agents in various cellular states.

Example 11 Discrimination of Pathways Regulation of Apoptosis

The present invention is useful for discriminating biologically activeagents and genes that act on different pathways. Pthways involved incellular apoptosis can be distinguished from those involved inregulation of adhesion molecules and cytokines in inflammation, andagents that modify these pathways can be identified.

A panel of assay combinations that reproduces aspects of the response ofthe endothelial cells to inflammatory processes and stimuli enhancingapoptosis is used. TNFα and ceramide are factors known to enhance cellapoptosis in endothelial cells (Slowik, Lab Invest. 77:257, 1997).Endothelial cells cultured under basal conditions display a low level ofcell damage as measured by release of cytoplasmic lactate dehydrogenasefrom cells into the supernatant. This level is enhanced in culturescomprising TNF-α, ceramide, or the combination of ceramide and TNF-α.

Retroviral vectors (FIG. 15) derived from the MSCV-based pMSCVneo vector(Clontech) are used to express genes in the cultured endothelial cells.Other standard vectors or tranfection protocols can be substituted. Testgenes are inserted downstream of the MSCV LTR, the marker gene is theenhanced green fluorescent protein (GFP) and the IRES is 600 bp fragmentfrom EMCV virus (Jang, J. Virol. 63:1651, 1989). In the example in FIG.17, test genes are human Bcl-2 and Bcl-xL. Retroviral vector plasmid DNAis transfected into AmphoPack-293 cells (Clontech) by modified calciumphosphate method according to manufacturer's protocol (MBS transfectionkit, Stratagene). Cell supernatants are harvested 48 hourspost-transfection, filtered to remove cell debris (0.45 μm) andtransferred onto exponentially growing HUVEC cells. DEAE dextran (conc100 μg/ml) is added to facilitate vector transduction. After a 5-8 hourincubation period viral supernatants are removed and cells cultured foran additional 40 hours. Gene transfer efficiency is determined by FACS,and is typically ≧80 percent. Transduced cells are re-plated into96-well plates for biomap analysis. Confluent HUVEC cells are treatedwith either ceramide (10 μm), TNF-α (5 ng/ml), ceramide (10 μm)+TNF-α (5ng/ml), or TNF-α (5 ng/ml)+IFN-g (100 ng/ml)+IL-1 (1 ng/ml), or mediaonly. After 24 hours, transduced cells are evaluated for the surfaceexpression ICAM-1 (1), VCAM-1 (2), and MIG (3) by cell-based ELISA forbiomap analysis. For the expanded biomap, cell supernatants at 24 hoursare collected and analyzed for the presence of LDH (4). In the presentexample, over-expression of Bcl-2 and Bcl-xL results in altered biomapparameters that reflect an effect on the apoptotic pathway (e.g. FIG.17, parameter 4, LDH), but not biomap parameters that reflect adhesionand cytokine regulation pathways (parameters 1, 2 and 3; ICAM-1, VCAM-1and MIG, respectively).

This example clearly shows the utility of biomap analysis fordistinguishing gene effects on multiple cell functions and pathways, andin the present example, for identifying genes modulating apoptosispathways.

Example 12 Function of Genes in Cellular Responses in InflammationAntisense Approach

The present invention is useful for identifying functions of genes andtheir expressed gene products using antisense approaches. For example,genes whose products regulate inflammation can be identified in aninflammation model using human endothelial cells as an indicator celltype. A panel of assay combinations that reproduce aspects of theresponse of the endothelial cells to different types of inflammatoryprocesses is used, as described in Example 1.

Primary human umbilical vein endothelial cells (HUVEC) are used. Othercells that may replace HUVEC in the screen include primary microvascularendothelial cells, aortic or arteriolar endothelial cells or endothelialcell lines such as EAhy926 or E6-E7 4-5-2G cells or human telomerasereverse transcriptase-expressing endothelial cells (Simmons, J.Immunol., 148:267, 1992; Rhim, Carcinogenesis 19:673, 1998; Yang, J.Biol. Chem. 274:26141,1999).

Morpholino phosphorodiamidate (MF) antisense oligonucleotides are used.Other chemical classes of antisense oligonucleotides that can besubstituted for morpholinos include but are not limited tophosphorotioate oligonucleotides, N3′-P5′ phosphoramidateoligonucleotides (NP), locked nucleic acid (LNA), 2′-O-methoxyethylnucleic acid (MOE), 2′-fluoro-arabinonucleic acid (FANA), peptidenucleic acids (PNA) (reviewed in Toulme, Nature Biotech. 19:17, 2001).In the example in FIG. 18, antisense oligonucleotides for TNF-R1 (p55)(5′-AGGTCAGGCACGGTGGAGAGGC-3′) (SEQ ID NO:1), and the beta-globincontrol oligo (5′-CCTCTTACCTCAGTTACAATTTATA-3′) (SEQ ID NO:2) (GeneTools Inc.) are used. The transfection mixture is prepared by mixing 5ml of stock morpholino (0.5 mM), 500 ml water, and 4 ml of 200 mM EPEI(Ethoxylated PolyEthylenimine), vortexed, incubated at room temperaturefor 20 minutes, and then mixed with 3.5 ml of serum-free media to give afinal 0.6 μM morpholino concentration. HUVEC cells are plated the daybefore in 24-well plates at 4-6×10e4 cells/well. Cells are washed oncewith serum-free media and incubated with 0.4 ml of the morpholinotransfection mixture at 37° C. for 3 hours. Morpholino is removed,regular media (Epithelial Growth Media with 2% fetal calf serum,Clonetics) is added and cells allowed to recover overnight. Theefficiency of loading of cells with morphino is monitored in cellsincubated with a fluorescent morpholino, and is typically essentially100 percent. HUVEC cells are then treated with either TNF-α (0.5 ng/ml),or IL-1 (1 ng/ml), or media only. After 4 hours, cells are harvested andevaluated for the cell surface expression of ICAM-1 (1), VCAM-1 (2),E-selectin (3), and CD31 (4) by flow cytometry. FIG. 18 shows thatTNF-R1 antisense gives an altered biomap that is distinct from thecontrol oligo biomap (with control morpholino) upon treatment withTNF-α, but not upon treatment with IL-1. The TNF-R1 antisensespecifically blocks induction of ICAM-1 and VCAM-1 by TNF-α, while ithas no effect on induction of the same markers by the independent cellsurface receptor for IL-1.

The results illustrate the utility of the invention in identifying thefunction of genes in different assay combinations in an assay panel.This example clearly shows the utility of biomap analysis fordistinguishing gene effects on multiple cell functions and pathways, andin the present example, for identifying genes involved in signaling by aproinflammatory cytokine.

Example 13 Cancer Applications Colon Carcinoma

The present invention is applied for the screening of compounds for usein treating colon carcinoma.

The human colon carcinoma cell line HT-29 is used. Other colon carcinomacells lines that may replace HT-29 in the screen include CaCo-2,Colo201, DLD-1, HCC 2998, HCT116, KM-12, LoVo, LS-174, SW-48, SW-480,SW-620, SW-83 or T-84; or primary tumor cells. 2×10⁴ cells/ml arecultured in McCoy's 5a Medium containing 1.5 mM L-glutamine and 10% FBS.Other media that may replace McCoy's 5a Medium include Eagle's MediumHam's F12 Medium, Dulbecco's Modified Eagle's Medium and chemicallydefined McCoy's 5A serum-free medium (Life Technologies, Inc.)supplemented with 20 μg/ml insulin, 4 μg/ml transferrin, and 10 ng/mlepidermal growth factor. Other conditions of interest that may be usedin subsequent assay combinations include assaying cultures with duringlog phase growth. Following overnight serum starvation the following arethen applied for 48 hours: IGF-II (10 nM), TGF-β (10 ng/ml), and TNF-α(100 ng/ml). In subsequent panels one or more of IL-1 (10 ng/ml), IL-4(20 ng/ml), IL-13 (30 ng/ml), TGF-β (10 ng/ml), IFN-γ (200 U/ml),epidermal growth factor (10 ng/ml) and IL-6; and/or neutralizingantibodies to autocrine factors, IGF-II, IL-8 or TGF-β or the receptorIGF-R I, are added to the initial three factors or may replace one ofthe three factors. Standard concentrations of agents are employed asdescribed in the literature (Freier, Gut 44:704, 1999; Naylor, CancerRes. 50:4436, 1990; Kanai, Br. J. Cancer 82:1717, 2000; Wright, J B C274:17193, 1999; Zarrilli, J B C 271:8108, 1996; Murata, J B C270:30829, 1996; Cardillo, J. Exp. Clin. Cancer Res. 16:281, 997;Rajagopal, Int. J. Cancer 62:661, 1995; Barth, Cancer 78:1168, 1996).Based on the parameters altered by the indicated factors, biomaps aregenerated for the parameters integrin α_(v), ICAM-1, CD44,carcinoembryonic antigen (CEA) and α₅β₁. Other markers of interest foradding to the biomap include EGF-R, HLA-Class I, HLA-DR,poly-Ig-receptor, IL-8, CA-19-9, E-cadherin, CD95, α₂β₁, α₃β₁, α₆β₁,α₆β₄, α_(v), laminin 5, urokinase-type plasminogen activator receptor(uPAR), MIP-3α and TNFR-I (Kelly, Am. J. Physiol. 263, G864-70, 1992;Moller, Int. J. Cancer, 57:371, 1994). Parameters of interest alsoinclude parameters indicative of cell damage and apoptosis includingreleased cytoplasmic lactate dehydrogenase (LDH) or mitochondrialcytochrome c, appearance of APO2.7 epitope or active caspase-3 (Zhang,J. Immunol., 157:3980, 1996; Bussing, Cytometry 37:133, 1999).Parameters indicative of cell proliferation are also of interest andinclude Ki-67 and PCNA (Landberg, Cytometry, 13:230, 1992).

A database of biomaps is generated from a panel of assay combinationsthat include the differentiation-inducing agent butyrate, and knownanti-cancer agents. DNA synthesis inhibitors, nucleoside analogs,topoisomerase inhibitors, and microtubule function inhibitors arescreened and a biomap generated that shows the changes in the markerswith the different anti-cancer agents. Such compounds are given inWeinstein, 1997, and The Pharmacologic Basis of Therapeutics. Thebiomaps with the known agents are used to compare to candidateanti-cancer drugs. This allows the recognition of the pathway(s) thecandidate anticancer drug acts on, by comparing the changes in the levelof the specific markers for known drugs affecting known pathways and thechanges observed with the candidate drug. In addition, to further add tothe utility of the biomap, one may include in the database referencebiomaps generated from assay panels containing cells with geneticconstructs that selectively target or modulate specific cellularpathways (e.g. ras, p53, NFκB, MAP kinase, etc), or cells that containknown genetic mutations (e.g. HT-29 cells contain a p53 mutation, etc.).

Example 14 Cancer Applications Prostate Cancer

The present invention is applied for the screening of compounds for usein treating prostate cancer.

The human prostate carcinoma cell line LNCaP is used. Other prostatecarcinoma cells lines that may replace LNCaP in the screen includeDU-145, PPC-1, PC-3, MDA PCA 2b, JCA-1; normal prostate epithelial cellsor primary tumor cells. 2×10⁴ cells/ml are cultured in Dulbecco'sModified Eagle's Medium (DMEM) containing 10% FBS. Other media that mayreplace Dulbecco's Modified Eagle's Medium include RPMI, HAMS F12, DMEMcontaining charcoal-stripped serum or serum-free DMEM supplemented with0.5% BSA. Other conditions of interest that may be used in subsequentassay combinations include assaying cultures with during log phasegrowth. Following overnight serum starvation the following are thenapplied for 24 hours: 5-dihydrotestosterone (10 nM), TNF-α (200 U/ml)and IL-6 (50 ng/ml). In subsequent panels one or more of IL-1 (10ng/ml), IFN-γ (500 U/ml), TGF-α (10 ng/ml), epidermal growth factor (10ng/ml) and IGF-II (10 nM); and/or neutralizing antibodies to autocrinefactors, IGF-II, EGF, IL-6 or TGF-β or their receptors; and/or hypoxicconditions are added to the initial three factors or may replace one ofthe three factors. Standard concentrations of agents are employed asdescribed in the literature (Sokoloff, Cancer 77:1862, 1996; Qiu, PNAS95:3644, 1998; Hsiao, J B C 274:22373, 1999). Based on the parametersaltered by the indicated factors, biomaps are generated for theparameters prostate specific antigen (PSA), E-cadherin, IL-8, epidermalgrowth factor receptor and vascular endothelial growth factor (VEGF).Other markers of interest for adding to the biomap include epidermalgrowth factor receptor, Her-2/neu EGF-R, HLA-class I, HLA-DR, CD95, α3,α₂β₁, α₅β₁, α_(v)β₃, ICAM-1, MIP-3α and CD44. Parameters of interestalso include parameters indicative of cell damage and apoptosisincluding released cytoplasmic lactate dehydrogenase (LDH) ormitochondrial cytochrome c, appearance of APO2.7 epitope or activecaspase-3 (Zhang, J. Immunol., 157:3980, 1996; Bussing, Cytometry37:133, 1999). Parameters indicative of cell proliferation are also ofinterest and include Ki-67 and PCNA (Landberg, Cytometry, 13:230, 1992).

A database of biomaps is generated from a panel of assay combinationsthat include the differentiation-inducing agents butyrate, calcitriol,and known anti-cancer agents that include anti-androgens, DNA synthesisinhibitors, nucleoside analogs, topoisomerase inhibitors, andmicrotubule function inhibitors. These factors are screened and a biomapgenerated that shows the changes in the markers with the differentanti-cancer agents. Such compounds are given in Weinstein, 1997, and ThePharmacologic Basis of Therapeutics. The biomaps with the known agentsare used to compare to candidate anti-cancer drugs. This allows therecognition of the pathway(s) the candidate anticancer drug acts on, bycomparing the changes in the level of the specific markers for knowndrugs affecting known pathways and the changes observed with thecandidate drug. In addition to further add to the utility of the biomap,one may include in the database reference biomaps generated from assaypanels containing cells with genetic constructs that selectively targetor modulate specific cellular pathways (e.g. ras, p53, NFκB, MAP kinase,etc), or cells that contain known genetic mutations (e.g. LNCaP cellscontain a K-ras mutation, etc.).

Example 15 Cancer Applications Breast Cancer

The present invention is applied for the screening of compounds for usein treating breast cancer.

The human breast cell line MCF-7 is used. Other breast cancer cell linesthat may replace MCF-2 in the screen include AU-565, HCC38, MCF-7,MDA-MB-231, MIB 157, SW-527, T47D, UACC-812, UACC— or ZR-75-1; primarymammary epithelial cells or primary tumor cells. 2×10⁴ cells/ml arecultured in RPMI medium 10% FBS. Other media that may replace RPMIinclude Dulbecco's Modified Eagle's Medium containing 20% FBS. Otherconditions of interest that may be used in subsequent assay combinationsinclude assaying cultures with during log phase growth. Followingovernight serum starvation the following are then applied for 24 hours:estrogen (10⁻⁷ M), IL-4 (50 ng/ml), antibody to Her-2/neu, IL-1β (10ng/ml). In subsequent panels one or more of IGF-I (5 nM), TNF-α (100ng/ml), IFN-γ (200 U/ml), IL-13 (30 ng/ml), TGF-β (10 ng/ml), epidermalgrowth factor (10 ng/ml) and IL-6; and/or neutralizing antibodies toautocrine factors, IL-1, TGF-β or the receptor IGF-R I, are added to theinitial three factors or may replace one of the three factors. Standardconcentrations of agents are employed as described in the literature(Jackson, J B C 273:9994, 1998; He, PNAS 97:5768, 2000). Based on theparameters altered by the indicated factors, biomaps are generated forthe parameters ICAM-1 (CD54), IL-8, MCP-1, E-cadherin, HLA-DR, CD44,carcinoembryonic antigen (CEA, CD66e), MIP-3α and α₅β₁. Other markers ofinterest for adding to the biomap include EGF-R, HLA-1,poly-Ig-receptor, IL-8, CA-19-9, CD95, α₂β₁, α₃β₁, α₆β₁, α₆β₄, α_(v),laminin 5, urokinase-type plasminogen activator receptor (uPAR), andTNFR-I. Parameters of interest also include parameters indicative ofcell damage and apoptosis including released cytoplasmic lactatedehydrogenase (LDH) or mitochondrial cytochrome c, appearance of APO2.7epitope or active caspase-3 (Koester, Cytometry, 33:324, 1998; Zhang, J.Immunol., 157:3980, 1996; Bussing, Cytometry 37:133, 1999). Parametersindicative of cell proliferation are also of interest and include Ki-67and PCNA (Landberg, Cytometry, 13:230, 1992).

A database of biomaps is generated from a panel of assay combinationsthat include the differentiation-inducing agent calcitriol, and knownanti-cancer agents. anti-estrogens, DNA synthesis inhibitors, nucleosideanalogs, topoisomerase inhibitors, and microtubule function inhibitorsare screened and a biomap generated that shows the changes in themarkers with the different anti-cancer agents. Such compounds are givenin Weinstein, 1997, and The Pharmacologic Basis of Therapeutics. Thebiomaps with the known agents are used to compare to candidateanti-cancer drugs. This allows the recognition of the pathway(s) thecandidate anticancer drug acts on, by comparing the changes in the levelof the specific markers for known drugs affecting known pathways and thechanges observed with the candidate drug. In addition to further add tothe utility of the biomap, one may include in the database referencebiomaps generated from assay panels containing cells with geneticconstructs that selectively target or modulate specific cellularpathways (e.g. ras, p53, NFκB, MAP kinase, etc), or cells that containknown genetic mutations (e.g. MDA-MB-231 cells contain a mutant p53,etc.).

Example 16 Angiogenesis Inhibitors

The present invention is applied for the screening of compounds thatinhibit or modulate angiogenesis for treatment of vascularizedneoplasms, rheumatoid arthritis and other disorders, or for conditionswhere vascular remodeling is beneficial.

Primary human umbilical vein endothelial cells are used. Other cellsthat may replace HUVEC in the screen include primary microvascularendothelial cells, aortic endothelial cells. 2×10⁴ cells/ml are culturedto confluence in EGM-2 (Clonetics). Other media that may replace EGM-2include EGM (Clonetics) and Ham's F12K medium supplemented with 0.1mg/ml heparin and 0.03-0.05 mg/ml endothelial cell growth supplement(ECGS) and 10% FBS, or medium M199 (Life Technologies, Inc.) containing20% fetal bovine serum and 2 ng/ml basic fibroblast growth factor(Jaffe, J. Clin. Invest. 52:2745, 1973; Hoshi, PNAS 81:6413, 1984).Following overnight serum starvation, the following are then applied for24 hours: VEGF (10 ng/ml), TNF-α (1 ng/ml) and bFGF (10 ng/ml). Insubsequent panels one or more of IL-4 (20 ng/ml), IL-13 (20 ng/ml), EGF(10 ng/ml), hydrocortisone (2 ng/ml), thrombin (0.1 U/ml), hypoxicconditions (Xu, J B C 275:24583, 2000); and/or neutralizing antibodiesto autocrine factors, such as TGF-β, IL-8 or IL-6 are added to theinitial three factors or may replace one of the three factors. Standardconcentrations of agents are employed as described in the literature(Thakker, J B C 274:10002, 1999; Kikuchi, NRMGK 23:12, 2000; Woltmann,Blood 95:3146, 2000; Wu, J B C 275:5096, 2000). Based on the parametersaltered by the indicated factors, biomaps are generated for theparameters alphavbeta3, IL-8, VCAM-1, von Willebrand factor, E-selectin,fibronectin and uPAR (Friedlander, Science 270:1500, 1995; Zanetta, Int.J. Cancer 85, 281, 2000). Other markers of interest for adding to thebiomap include: thrombomodulin, Tissue Factor, MMP-2, MMP-3, α₅β₁,α_(v)β₅, CD105, CXCR4 and CD31 (St. Croix, Science 289:1197, 2000;Friedlander, Science 270:1500, 1995; Bodey, Anticancer Res. 18:3621,1998).

A database of biomaps is generated from a panel of assay combinationsthat include the known angiogenesis inhibitors and agents are screenedand a biomap generated that shows the changes in the markers with thedifferent anti-angiogenesis agents. Such anti-angiogenic compoundsinclude growth factor signaling inhibitors and are given in ThePharmacologic Basis of Therapeutics. The biomaps with the known agentsare used to compare to candidate anti-angiogenic drugs. This allows therecognition of the pathway(s) the candidate anti-angiogenic drug actson, by comparing the changes in the level of the specific markers forknown drugs affecting known pathways and the changes observed with thecandidate drug. In addition, to further add to the utility of thebiomap, one may include in the database reference biomaps generated fromassay panels containing cells with genetic constructs that selectivelytarget or modulate specific cellular pathways (e.g. ras, rho, NFκB, MAPkinase, etc), (e.g. HUVEC retrovirally transformed to overexpress bcl-2(Zheng, J. Immunol 164:4665, 1999) or cells that contain known geneticmutations.

Example 17 Cardiovascular Disease

The present invention is applied for the screening of compounds for usein treating vascular dysfunction associated with cardiovascular disease,hypertension, diabetes and autoimmune disease.

Human aortic endothelial cells are used. Other cells that may replacehuman aortic endothelial cells include: human umbilical vein endothelialcells and human microvascular endothelial cells. 2×10⁴ cells/ml arecultured to confluence in Endothelial cell growth medium-2 (EGM-2,Clonetics Corp.) containing Epidermal Growth Factor (100 ng/ml),hydrocortisone (1 ug/ml), Vascular Endothelial Growth Factor (10 ng/ml),Fibroblast Growth Factor B (30 ng/ml), Insulin Like Growth Factor-1 (10nM) and 2% FBS. Other media that may replace EGM-2 include Medium 199containing ECGF (50 ug/ml) and heparin (100 ug/ml); Medium 199supplemented with 10% FBS; or endothelial cell basal medium (CloneticsCorp.) containing 1% bovine serum albumin (Thornton, Science 222:623,1983; Jaffe, J. Clin. Invest, 52:2745, 1974; Wu, J. Biol. Chem.275:5096, 2000). The following are then applied for 24 hours:angiotensin-II (100 nM), TNF-α (5 ng/ml) and thrombin (10 U/ml) (Dietz,Basic Res. Cardiology 93 Suppl2:101, 1998; Lommi, Eur. Heart. J.18:1620, 1997; Jafri, Semin. Thromb. Hemost. 23:543, 1997). Insubsequent panels one or more of IL-1 (10 ng/ml), IFN-γ (100 ng/ml) IL-4(20 ng/ml), IL-13 (30 ng/ml), TGF-beta (10 ng/ml), endothelin-1 (100nM), aldosterone (1 uM), oxidized LDL (100 ug/ml), or minimally modifiedLDL are added to the initial three factors or may replace one of thethree factors (Brown, J Clin Endocrinol Metab, 85:336, 2000; de Boer, J.Pathol. 188:174, 1999; Berliner, J. Clin. Invest. 85:1260, 1990).Standard concentrations of agents are employed as described in theliterature (Kaplanski, J. Immunol. 158:5435, 1997; Hofman, Blood92:3064, 1998; Li, Circulation 102:1970, 2000; Essler, J B C 274:30361,1999; Brown, J Clin Endocrinol Metab, 85:336, 2000). Based on theparameters altered by the indicated factors, biomaps are generated forthe parameters ICAM-1, vWF, E-selectin, P-selectin, IL-8, PAI-1,angiotensin converting enzyme (ACE, CD143), platelet-derived growthfactor (PDGF) and MCP-1 (Devaux, Eur. Heart J. 18:470, 1997; Kessler,Diabetes Metab. 24:327, 1998; Becker, Z. Kardiol. 89:160, 2000;Kaplanski, J. Immunol. 158:5435, 1997; Li, Circulation 102:1970, 2000).Other markers of interest for adding to the biomap include,angiotensin-1 receptor, urokinase-type plasminogen activator receptor(uPAR, CD87), endothelin-1 receptor, tissue factor (CD142),fibrinogen-binding activity, MIG chemokine, and CD36 (Paramo, Br. Med.J. 291:573, 1985; Fukuhara, Hypertension 35:353, 2000; Noda-Heiny,Arterioscler Thromb Vasc. Biol. 15:37, 1995; de Prost, J. Cardiovasc.Pharmacol., 25 Suppl2:S114, 1995; van de Stolpe, Thromb Haemost 75:182,1996; Mach, J. Clin. Invest., 104:1041, 1999; Nicholson, Ann. N.Y. Acad.Sci., 902:128, 2000). A database of biomaps is generated from a panel ofassay combinations that include known cardioprotective agents includingbeta blockers and other hypertensive drugs, ACE inhibitors, AT1antagonists, and anti-aldosterones; statins; and others, are screenedand a biomap generated that shows the changes in the markers with thedifferent anti-cancer agents. Such compounds are given in ThePharmacologic Basis of Therapeutics. The biomaps with the known agentsare used to compare to candidate cardioprotective drugs. This allows therecognition of the pathway(s) the candidate drug acts on, by comparingthe changes in the level of the specific markers for known drugsaffecting known pathways and the changes observed with the candidatedrug. In addition to further add to the utility of the biomap, one mayinclude in the database reference biomaps generated from assay panelscontaining cells with genetic constructs that selectively target ormodulate specific cellular pathways (e.g. NFκB, MAP kinase, etc), orcells that contain known genetic mutations (e.g. CD36-deficiency, Yanai,Am. J. Med. Genet. 93:299, 2000, etc.).

Example 18 Regulators of T Cell Function Naive T Cell Responses

The present invention is useful for identifying regulators of T cellmediated inflammation and immunity. A set of assay combinations thatreproduces aspects of the response of the naïve T cells are used.

The immune cell stimulatory environment in vivo during pathogenicimmunity is characterized by the presence of multiple biologicallyactive agents including IL-1, IL-2, TNF-α, and IFN-γ, IL4, IL12, IL10,TGF beta, IL6, IL7 and IL15 and others (Picker, J. Immunol. 150:1122,1993; Picker J. Immunol: 150:1105, 1993; W. Paul, FundamentalImmunology, 4th Ed, 1998. Lippincott Williams & Wilkins Publishers).Optimized assay combinations for naïve T cell responses will contain atleast two, and preferably three, four or more of these biologicallyactive agents in addition with a primary stimulus through the T cellreceptor and secondary stimuli through co-stimulatory receptors.Concentrations of agents are standard according to the literature.Concentrations may also be determined experimentally as the amountrequired to saturate the relevant receptor.

Primary human cord blood mononuclear cells are used. Other cells thatmay replace these cells include isolated populations of naïve CD4+and/or CD8+ T cells isolated from adult human peripheral blood T cells.Cord blood mononuclear cells are isolated from blood by Ficoll-hypaquedensity gradient centrifugation as described (Ponath, JEM 183:2437,1996). Cells are then cultured at 106 cells/ml in RPMI containing 10%FBS and Staphylococcal Enterotoxin B (SEB) (1 ug/ml), anti-CD28 (10ug/ml), and IL-12 (5 ng/ml). In subsequent panels one or moreStaphylococcal Enterotoxin E (SEE), or toxic shock syndrome toxin(TSST), or antibody to CD3 (1 ug/ml) can replace or be included with SEBto provide T cell receptor stimulation. TCR stimulation throughconventional antigens or alloantigen, as in the mixed lymphocyteculture. In subsequent panels one or more of IL-1 (10 ng/ml), IL-2 (1ng/ml), IL-10, IL-4 (20 ng/ml), IL-13 (30 ng/ml), TGF-b (10 ng/ml),anti-CD49d, IL-6, IL-7, IL-15, IL-18; and/or neutralizing antibodies toautocrine factors, IL-2, TNF-α, are added to the initial three factorsor may replace the IL-12. Antibodies or ligands for CD49d and CD28provide costimulatory signals. Other Alternative costimulatory signalsof interest that may be substituted for anti-CD28 and anti-CD49d includeantibodies or ligands to CD5, anti-ICOS, or anti-4-1BB. The TCR stimulusand biologically active factors are then applied for 24 hours: Othertime points of interest include 6 hours, 72 hours or 5 days.

Based on the parameters altered by the indicated factors, biomaps aregenerated for the parameters CD69, alphaEbeta7 (CD103), IL-12Rβ2(CD212), CD40L (CD154), intracellular TNF-α, intracellular IL-2 andCXCR3. Other markers of interest for adding to the biomap include:ICAM-1, alpha4beta7, cutaneous lymphocyte antigen (CLA), CD40L (CD154),OX40 (CD134), FasL (CD178), CTLA-4 (CD152), L-selectin (CD62L), CCR5,CCR6, CCR7, CXCR4, CXCR5, IL-4R(CD124), CD26, CD38, CD30, intracellularIFN-γ, intracellular IL-4, CD25, CCR9, CCR2, CCR4, RANTES, MIP-1 beta,CD71, CD223, ICOS, CDw137.

Parameters on T cells in the culture are analyzed by flow cytometry.Anti-CD3 and anti-CD4 antibodies are used to identify CD4+ and CD4− Tcells, and non T cells. Antibodies to the selected parameters are usedwith two additional colors. Readout patterns for T cells cultured withand without SEB or costimulators can be distinguished.

A database of biomaps is generated from a panel of assay combinationsthat include the presence and absence of each biologically activefactor; and anti-inflammatory drug compounds including inhibitors of Tcell activation and/or T cell proliferation including calcineurininhibitors, FK506, cyclosporin, mycophenolic acid, methotrexate; as wellas immune stimulatory agents including pathogens or pathogen components,etc. are screened and biomaps generated that show the changes in themarkers with the different agents. Such agents are given in ThePharmacologic Basis of Therapeutics. The biomaps with the known agentsare used to compare to candidate drugs. This allows the recognition ofthe pathway(s) the candidate drug acts on, by comparing the changes inthe level of the specific markers for known drugs affecting knownpathways and the changes observed with the candidate drug. In additionto further add to the utility of the biomap, one may include in thedatabase reference biomaps generated from assay panels containing cellswith genetic constructs that selectively target or modulate specificcellular pathways (e.g. NFAT, calcineurin, NFκB, MAP kinase, etc), orcells that contain known genetic mutations, e.g. Jurkat cell lines thatlack Ick, CD45, etc. (Yamasaki, J. Biol. Chem. 272:14787,1997).

Example 19 Regulators of T Cell Function Adult and Memory T Cells

The present invention is useful for identifying regulators of T cellmediated inflammation and immunity. A set of assay combinations thatreproduces aspects of the response of the adult T cells is used.

Adult human peripheral blood mononuclear cells are used. Other cellsthat may replace adult peripheral blood T cells include isolatedpopulations of CD4+, CD8+, TCRgamma/delta, and/or memory T cells; T celllines such as Jurkat, Hut 78, CEM, and T cell clones. Peripheral bloodmononuclear cells are isolated from blood by Ficoll-hypaque densitygradient centrifugation as described (Ponath, JEM 183:2437, 1996). Cellsare then cultured at 106 cells/ml in RPMI containing 10% FBS andStaphylococcal Enterotoxin B (SEB) (1 μg/ml), anti-CD28 (10 ug/ml), andIL-12 (5 ng/ml). In subsequent panels one or more StaphylococcalEnterotoxin E (SEE), or toxic shock syndrome toxin (TSST), or antibodyto CD3 (1 ug/ml) can replace or be included with SEB to provide T cellreceptor stimulation. TCR stimulation through conventional antigens oralloantigen, as in the mixed lymphocyte culture. In subsequent panelsone or more of IL-1 (10 ng/ml), IL-2 (1 ng/ml), IL-10, IL-4 (20 ng/ml),IL-13 (30 ng/ml), TGF-beta (10 ng/ml), anti-CD49d, IL-6, IL-7, IL-15,IL-18; and/or neutralizing antibodies to autocrine factors, IL-4, IFN-γ,IL-2, TNF-α, are added to the initial three factors or may replace theIL-12. Antibodies or ligands for CD49d and CD28 provide costimulatorysignals. Other alternative costimulatory signals of interest that may besubstituted for anti-CD28 and anti-CD49d include antibodies or ligandsto CD5, anti-ICOS, or anti-4-1BB. The TCR stimulus and biologicallyactive factors are then applied for 24 hours: Other time points ofinterest include 6 hours, 72 hours or 5 days.

Standard concentrations of agents and factors are employed as describedin the literature. T cells in the cultures are analyzed by flowcytometry. Based on the parameters altered by the indicated factors,biomaps are generated for the parameters CD40L (CD154), CD69, Ox40(CD134), intracellular γIFN, TNFα, IL-2, FAS ligand (CD178), alphae-integrin (CD103), CTLA4 (CD152), and IL-12receptor beta2 (CD212).Other parameters of interest include CD95, CD45RO, alph4beta7,alpha4beta7, alpha4beta1, L-selectin (CD62L), CCR7, CCR5, CXCR3, CXCR4,CCR6, CXCR5, CCR9, CCR2, CCR4, RANTES, MlP1beta, CD71, CD223, ICOS,CDw137, CD26, CD30, CD38, cutaneous lymphocyte antigen (CLA) and IL-4Ralpha chain.

Parameters on T cells in the culture are analyzed by flow cytometry.Anti-CD3 and anti-CD4 antibodies are used to identify CD4+ and CD4− Tcells, and non T cells. CD95, CD45RO and/or CD45RA are used to identifymemory T cells. Antibodies to the selected parameters are used with 2-4additional colors. Readout patterns for T cells cultured with andwithout SEB or costimulators can be distinguished.

A database of biomaps is generated from a panel of assay combinationsthat include the presence and absence of each biologically activefactor; and anti-inflammatory drug compounds including inhibitors of Tcell activation and/or T cell proliferation including calcineurininhibitors, FK506, cyclosporin, mycophenolic acid, methotrexate; as wellas immune stimulatory agents including pathogens or pathogen components,etc. are screened and biomaps generated that show the changes in themarkers with the different agents. Such compounds are given in ThePharmacologic Basis of Therapeutics. The biomaps with the known agentsare used to compare to candidate agents. This allows the recognition ofthe pathway(s) the candidate agent acts on, by comparing the changes inthe level of the specific markers for known drugs affecting knownpathways and the changes observed with the candidate agent. In additionto further add to the utility of the biomap, one may include in thedatabase reference biomaps generated from assay panels containing cellswith genetic constructs that selectively target or modulate specificcellular pathways (e.g. NFAT, calcineurin, NFκB, MAP kinase, etc), orcells that contain known genetic mutations, e.g. Jurkat cell lines thatlack Ick, CD45, etc. (Yamasaki, J. Biol. Chem. 272:14787,1997).

Example 20 Regulators of T Cell Function Th1 Responses

The present invention is applied for the screening of compounds thatinhibit the activation of Th1 lymphocytes.

Human peripheral blood CD4+ T cells are employed. Other cells that maybe employed include the T cell line KIT-225, human peripheral blood CD3+cells, human cord blood T cells, etc. Cells are isolated from humanperipheral blood mononuclear cells utilizing Ficoll-hypaque densitygradient centrifugation as described (Ponath, JEM 183:2437, 1996).Following adherence of cells to plastic, CD4+ cells are isolated fromnon-adherent cells using Miltenyi magnetic beads as described (Andrew, JI 166:103, 2001). Alternatively, purified human CD4+ T cells areobtained from a commercial source (Clonetics Corp.). Purified CD4+lymphocytes are then cultured at 10⁶ cells/ml in DMEM containing 10% FBSand anti-CD3 (1 μg/ml), anti-CD28 (10 μg/ml), IL-2 (4 ng/ml), IL-12 (5ng/ml) and neutralizing antibody to IL-4 (1 μg/ml) for 3 days. Insubsequent panels one or more of PHA (1 μg/ml) IL-1 (20 ng/ml), IL-6,IL-7, neutralizing antibody to IL-4, are added to the initial threefactors or may replace one of the three factors. Other time points ofinterest include 5 and 7 days.

Based on the parameters altered by the indicated factors, biomaps aregenerated for the parameters CD40L (CD154), alpha4beta7, cutaneouslymphocyte antigen (CLA), CXCR3 (CD183), IL-12receptor beta2 (CD212),intracellular IFN-γ, intracellular TNF-α, and intracellular IL-2. Othermarkers of interest for adding to the biomap include: ICAM-1, OX40(CD134), FasL (CD178), CTLA-4 (CD152), L-selectin (CD62L), CCR5 (CD195),CCR6, CCR7 (CDw197), CXCR4 (CD184), CXCR5, IL-4R(CD124), CD26, CD38,CD30, P-selectin-ligand activity, intracellular IL-4, intracellur IL-5and intracellular IL-13.

Parameters on T cells in the culture are analyzed by flow cytometry.Anti-CD3 and anti-CD4 antibodies are used to identify CD4+ and CD4− Tcells, and non T cells. CD45RO and/or CD45RA are used to identify memoryT cells. Antibodies to the selected parameters are used with 2-4additional colors. Readout patterns for T cells cultured with andwithout SEB or costimulators can be distinguished.

A database of biomaps is generated from a panel of assay combinationsthat include the presence and absence of each biologically activefactor; and anti-inflammatory drug compounds including inhibitors of Tcell activation and/or T cell proliferation including calcineurininhibitors, FK506, cyclosporin, mycophenolic acid, methotrexate; as wellas immune stimulatory agents including pathogens or pathogen components,etc. are screened and biomaps generated that show the changes in themarkers with the different agents. Such compounds are given in ThePharmacologic Basis of Therapeutics. The biomaps with the known agentsare used to compare to candidate agents. This allows the recognition ofthe pathway(s) the candidate agent acts on, by comparing the changes inthe level of the specific markers for known drugs affecting knownpathways and the changes observed with the candidate agent. In additionto further add to the utility of the biomap, one may include in thedatabase reference biomaps generated from assay panels containing cellswith genetic constructs that selectively target or modulate specificcellular pathways (e.g. NFAT, calcineurin, NFκB, MAP kinase, etc), orcells that contain known genetic mutations, e.g. Jurkat cell lines thatlack Ick, CD45, etc. (Yamasaki, J. Biol. Chem. 272:14787, 1997).

Example 21 Regulators of T Cell Function Th2 Responses

The present invention is applied for the screening of compounds thatinhibit the activation of Th2 lymphocytes.

Human peripheral blood CD4+ T cells are employed. Other cells that maybe employed include human peripheral blood CD3+ cells. Cells areisolated from human peripheral blood mononuclear cells utilizingFicoll-hypaque density gradient centrifugation as described (Ponath, JEM183:2437, 1996). Following adherence of cells to plastic, CD4+ cells areisolated from non-adherent cells using Miltenyi magnetic beads asdescribed (Andrew, JI 166:103, 2001). Purified CD4+ lymphocytes are thencultured at 10⁶ cells/ml in DMEM containing 10% FBS and anti-CD3 (1μg/ml), anti-CD28 (10 μg/ml 1) IL-2 (4 ng/ml), IL-4 (5 ng/ml) andneutralizing antibody to IFN-γ (1 μg/ml) for 3 days. In subsequentpanels one or more of PHA (1 μg/ml) IL-1 (20 ng/ml), IL-6, IL-7, IL-13,neutralizing antibody to IL-12, are added to the initial three factorsor may replace one of the three factors. Other time points of interestinclude 5 and 7 days.

Based on the parameters altered by the indicated factors, biomaps aregenerated for the parameters alpha4beta7, alphaEbeta7, cutaneouslymphocyte antigen (CLA), CCR3, intracellular IL-2, intracellular TNF-α,IL-4, IL-5 and IL-13. Other markers of interest for adding to the biomapinclude: ICAM-1, CD40L, OX40 (CD134), FasL (CD178), CTLA-4 (CD152),L-selectin (CD62L), CCR3, CCR5, CCR6, CCR7, CXCR4, CXCR5, IL-4R (CD124),CD26, CD38, CD30, P-selectin ligand activity and intracellular IFN-γ.

Parameters on T cells in the culture are analyzed by flow cytometry.Anti-CD3 and anti-CD4 antibodies are used to identify CD4+ and CD4− Tcells, and non T cells. CD45RO and/or CD45RA are used to identify memoryT cells (Teraki, J. Immunol. 159:6018, 1997; Waldrop, J. Immunol.161:5284, 1998; Picker, Blood, 86:1408, 1995). Antibodies to theselected parameters are used with 2-4 additional colors. Readoutpatterns for T cells cultured with and without SEB or costimulators canbe distinguished.

A database of biomaps is generated from a panel of assay combinationsthat include the presence and absence of each biologically activefactor; and anti-inflammatory drug compounds including inhibitors of Tcell activation and/or T cell proliferation including calcineurininhibitors, FK506, cyclosporin, mycophenolic acid, methotrexate; as wellas immune stimulatory agents including pathogens or pathogen components,etc. are screened and biomaps generated that show the changes in themarkers with the different agents. Such compounds are given in ThePharmacologic Basis of Therapeutics. The biomaps with the known agentsare used to compare to candidate agents. This allows the recognition ofthe pathway(s) the candidate agent acts on, by comparing the changes inthe level of the specific markers for known drugs affecting knownpathways and the changes observed with the candidate agent. In additionto further add to the utility of the biomap, one may include in thedatabase reference biomaps generated from assay panels containing cellswith genetic constructs that selectively target or modulate specificcellular pathways (e.g. NFAT, calcineurin, NFkB, MAP kinase, etc), orcells that contain known genetic mutations, e.g. Jurkat cell lines thatlack Ick, CD45, etc. (Yamasaki, J. Biol. Chem. 272:14787,1997).

Example 22 Regulators of Monocyte Functions

The present invention is applied for the screening of compounds formodulating monocyte/macrophage functions.

Human peripheral blood monocytes are used. Other cells that may replacehuman peripheral blood monocytes include: bone-marrow derived monocytes,monocytes isolated by elutriation or negative magnetic bead isolation,and monocyte cell lines THP-1 or U937. Four×10⁶ peripheral bloodmononuclear cells/ml are cultured in RPMI containing 10% fetal bovineserum for 2 hours. Non-adherent lymphocytes are removed by gentlewashing. The following are then applied for 24 hours: IL-1 (1 ng/ml),IFN-γ (50 ng/ml) and TGF-beta (10 ng/ml) (Dietz, Basic Res. Cardiology93 Suppl2:101, 1998; Lommi, Eur. Heart. J. 18:1620, 1997; Jafri, Semin.Thromb. Hemost. 23:543, 1997). In subsequent panels one or more oflipopolysaccharide (10 ng/ml), GM-CSF (10 ng/ml), TNF-α (5 ng/ml), IL-4(20 ng/ml), IL-13 (30 ng/ml), osteopontin (10 ng/ml), thrombin (10U/ml), CD40L, oxidized LDL (100 ug/ml), or minimally modified LDL areadded to the initial three factors or may replace one of the threefactors (Brown, J Clin Endocrinol Metab, 85:336, 2000; Ashkar, Science287:860, 2000; de Boer, J. Pathol. 188:174, 1999; Berliner, J. Clin.Invest. 85:1260, 1990). Standard concentrations of agents are employedas described in the literature (Kaplanski, J. Immunol. 158:5435, 1997;Hofman, Blood 92:3064, 1998; Li, Circulation 102:1970, 2000; Essler, J BC 274:30361, 1999; Brown, J Clin Endocrinol Metab, 85:336, 2000). Basedon the parameters altered by the indicated factors, biomaps aregenerated for the parameters ICAM-1, Mac-1 (CD11b/CD18), IL-8, HLA-DR,TNF-α, IL-12 and MCP-1 (Devaux, Eur. Heart J. 18:470, 1997; Kessler,Diabetes Metab. 24:327, 1998; Becker, Z. Kardiol. 89:160, 2000;Kaplanski, J. Immunol. 158:5435, 1997; Li, Circulation 102:1970, 2000).Other markers of interest for adding to the biomap include CD14, PAI-1,urokinase-type plasminogen activator receptor (uPAR, CD87), IL-10,IL-18, tissue factor, fibrinogen-binding activity, MIG chemokine, TARC,MDC, RANTES, CD25, CD80, CD86, CD40 and CD36 (Paramo, Br. Med. J.291:573, 1985; Fukuhara, Hypertension 35:353, 2000; Noda-Heiny,Arterioscler Thromb Vasc. Biol. 15:37, 1995; de Prost, J. Cardiovasc.Pharmacol., 25 Suppl2:S114, 1995; van de Stolpe, Thromb Haemost 75:182,1996; Mach, J. Clin. Invest., 104:1041, 1999; Nicholson, Ann. N.Y. Acad.Sci., 902:128, 2000). A database of biomaps is generated from a panel ofassay combinations that include known anti-atherogenic agents includingstatins and others, are screened and a biomap generated that shows thechanges in the markers with the different anti-cancer agents. Suchcompounds are given in The Pharmacologic Basis of Therapeutics. Thebiomaps with the known agents are used to compare to candidate agents.This allows the recognition of the pathway(s) the candidate drug actson, by comparing the changes in the level of the specific markers forknown drugs affecting known pathways and the changes observed with thecandidate drug. In addition to further add to the utility of the biomap,one may include in the database reference biomaps generated from assaypanels containing cells with genetic constructs that selectively targetor modulate specific cellular pathways (e.g. NFκB, MAP kinase, etc), orcells that contain known genetic mutations (e.g. CD36-deficiency, Yanai,Am. J. Med. Genet. 93:299, 2000, etc.).

It is to be understood that this invention is not limited to theparticular methodology, protocols, cell lines, animal species or genera,and reagents described, as such may vary. It is also to be understoodthat the terminology used herein is for the purpose of describingparticular embodiments only, and is not intended to limit the scope ofthe present invention which will be limited only by the appended claims.

As used herein the singular forms “a”, “and”, and “the” include pluralreferents unless the context clearly dictates otherwise. All technicaland scientific terms used herein have the same meaning as commonlyunderstood to one of ordinary skill in the art to which this inventionbelongs unless clearly indicated otherwise.

The examples are put forth so as to provide those of ordinary skill inthe art with a complete disclosure and description of how to make anduse the subject invention, and are not intended to limit the scope ofwhat is regarded as the invention. Efforts have been made to ensureaccuracy with respect to the numbers used (e.g. amounts, temperature,concentrations, etc.) but some experimental errors and deviations shouldbe allowed for. Unless otherwise indicated, parts are parts by weight,molecular weight is average molecular weight, temperature is in degreescentigrade; and pressure is at or near atmospheric.

All publications mentioned herein are incorporated herein by referencefor the purpose of describing and disclosing, for example, the compoundsand methodologies that are described in the publications which might beused in connection with the presently described invention. Thepublications discussed above and throughout the text are provided solelyfor their disclosure prior to the filing date of the presentapplication. Nothing herein is to be construed as an admission that theinventors are not entitled to antedate such disclosure by virtue ofprior invention.

1-32. (canceled)
 33. A method for characterization of a biologically active agent according to its mechanism of action, the method comprising: contacting said agent with cells in at least one cell culture assay combination comprising at least two factors acting on said cells; recording changes in at least three different cellular parameter readouts as a result of introduction of said agent; deriving a biomap from said changes in parameter readouts, wherein said biomap comprises data normalized to be a ratio of test to control data on the same cell type under control conditions in the absence of said biologically active agent, and said parameters are optimized so that the set of data in the biomap is sufficiently informative that it can discriminate the mechanism of action of said agent; analyzing said biomap by a multiparameter pattern recognition algorithm to quantify relatedness of said biomap to reference biomaps that include predetermined agents that target specific pathways, wherein the presence or absence of relatedness to said reference biomaps provides a characterization of said agent mechanism of action.
 34. The method of claim 33, wherein said analyzing step comprises: classifying said biomap for a single biologically active agent for relatedness to a plurality of biomaps in a database.
 35. The method of claim 33, wherein said biologically active agent is an organic compound.
 36. The method of claim 33, wherein the cells are primary human endothelial cells, and the at least two factors are selected from IL1β, TNFα, and IFNγ.
 37. The method of 36, wherein the parameters are selected from E-selectin, VCAM-1, ICAM-1, MCP-1, Mig, IL-8, HLA-DR, uPAR, Tissue Factor and SRB.
 38. The method of claim 33, wherein the cells are primary human endothelial cells, and the at least two factors are IL4 and histamine.
 39. The method of 38, wherein the parameters are selected from VEGFRII, P-selectin, VCAM-1, uPAR, Eotaxin-3, MCP-1 and SRB.
 40. The method of claim 33, wherein the cells are primary human fibroblasts and primary human keratinocytes, and the at least two factors are selected from IL1β, TNFα, IFNγ and TGFβ.
 41. The method of 40, wherein the parameters are selected from MCP-1, ICAM-1, Collagen I, IP-10, Mig, IL1α, M-CSF, PAI-1, TGFβ1, and uPAR.
 42. The method of claim 33, wherein the cells are primary human endothelial cells and human smooth muscle cells, and the at least two factors are selected from IL1β, TNFα, and IFNγ.
 43. The method of 42, wherein the parameters are selected from endothelin-1, E-selectin, MCP-1, VCAM-1, thrombomodulin, tissue factor, uPAR, IL-8, MIG, HGLA-DR, LDL-r, m-CSF and SRB.
 44. The method of claim 33, wherein the cells are human smooth muscle cells, and the at least two factors are selected from IL-1β, TNFα and IFNγ.
 45. The method of 44, wherein the parameters are selected from MCP-1, VCAM-1, thrombomodulin, tissue factor, uPAR, IL-8, MIG, HLA-DR, LDL-r, m-CSF and SRB.
 46. The method of claim 33, wherein the cells are human primary endothelial cells and Th2 T cells, and the at least two factors are selected from TCR stimulation, IL-12, and IL-2.
 47. The method of 46, wherein the parameters are selected from MCP-1, eotaxin 3, VCAM-1, CD38, CD40, E-selectin, uPAR, IL-8, CD69, HGLA-DR, MIG and SRB.
 48. The method of claim 33, wherein the cells are primary human endothelial cells and the at least two factors are TNFα and IL-4.
 49. The method of claim 33, wherein the cells are primary human endothelial cells and the at least two factors are TNFα and IL-4.
 50. The method of claim 33, wherein the cells are fibroblasts and the at least two factors are selected from IL1, TNFα, IFNγ, EGF, FGF and PDGF. 